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Selected Readings –December 2010 1

SELECTED READINGS

Focus on: Composite indicators

December 2010


INDEX

INTRODUCTION............................................................................................................. 7

1 WORKING PAPERS AND ARTICLES ................................................................ 8

1.1 Castillo C. and Lorenzana T., 2010, “Evaluation of Business Scenarios By Means Of Composite Indicators”, International Association for Fuzzy-set Management and Economy (SIGEF), Fuzzy economic review, Volume XV, Issue 1, Pages: 3-20. ................................................8

1.2 Jürgen Bierbaumer-Polly, 2010, “Composite Leading Indicator for the Austrian Economy. Methodology and "Real-time" Performance”, WIFO Working Papers No. 369..............................8

1.3 Heike Belitz, Marius Clemens, Astrid Cullmann, Christian von Hirschhausen, Jens Schmidt-Ehmcke, Doreen Triebe and Petra Zloczysti, 2010, “Innovation Indicator 2009: Germany Has Still Some Catching Up to Do”, DIW Berlin, German Institute for Economic Research, journal Weekly Report, 2010,Issue 3,Pages: 13-19. ...........................................................9

1.4 Grupp Hariolf and Schubert Torben, 2010, “Review and new evidence on composite innovation indicators for evaluating national performance”, Elsevier Research Policy, Volume 39, Issue 1, Pages: 67-78. ......................................................................................................................10

1.5 Laura Trinchera and Giorgio Russolillo, 2010, “On the use of Structural Equation Models and PLS Path Modeling to build composite indicators”, Macerata University, Department of Studies on Economic Development (DiSSE) Working Papers No. 30-2010.....................................11

1.6 Mohamed Daly Sfia, 2010, “A Composite Leading Indicator of Tunisian Inflation”, William Davidson Institute, University of Michigan, William Davidson Institute Working Papers Series No. wp980. .............................................................................................................................................11

1.7 Gómez-Limón José A. and Sanchez-Fernandez Gabriela, 2010, “Empirical evaluation of agricultural sustainability using composite indicators”, Elsevier, Ecological Economics, Volume 69, Issue 5, Pages: 1062-1075. ..............................................................................................................12

1.8 Hiroshi Yamada, Syuichi Nagata and Yuzo Honda, 2010, “A comparison of two alternative composite leading indicators for detecting Japanese business cycle turning points”, Taylor and Francis Journals, Applied Economics Letters, Volume 17, Issue 9, Pages: 875-879. .....................13

1.9 Michael Graff, 2010, “Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland”, Taylor and Francis Journals, Applied Economics, Volume 42, Issue 21, Pages: 2759-2781...........................................................................13

1.10 Ferdinand Fichtner, Rasmus Rüffer and Bernd Schnatz, 2009, “Leading indicators in a globalised world”, European Central Bank, Working Paper Series No. 1125. ...............................14

1.11 Salvati Luca and Zitti Marco, 2009, “Substitutability and weighting of ecological and economic indicators: Exploring the importance of various components of a synthetic index”, Elsevier in its journal Ecological Economics, Volume 68, Issue 4, Pages: 1093-1099.....................14

1.12 James E. Foster, Mark McGillivray and Suman Seth, 2009, “Rank Robustness of Composite Indices”, Queen Elizabeth House, University of Oxford, OPHI Working Papers No. ophiwp26. ..............................................................................................................................................15

1.13 Giuseppe Munda and Michela Nardo, 2009, “Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting”, Taylor and Francis, journal Applied Economics, Volume 41, Issue 12, Pages: 1513-1523...........................................................................16


1.14 L. Clavel and C. Minodier, 2009, “A Monthly Indicator of the French Business Climate”, Institut National de la Statistique et des Etudes Economiques, , Documents de Travail de la DESE - Working Papers of the DESE No. g2009-02. ........................................................................17

1.15 Laurens Cherchye, Willem Moesen, Nicky Rogge and Tom Van Puyenbroeck, 2009, “Constructing a knowledge economy composite indicator with imprecise data”, Katholieke Universiteit Leuven, Center for Economic Studies, Discussion papers No. ces09.15. ....................17

1.16 Luciana Crosilla, Solange Leproux, Marco Malgarini and Francesca Spinelli, 2009, “Factor based Composite Indicators for the Italian Economy”, ISAE Working Papers No. 116. ..............18

1.17 Harry P. Bowen and Wim Moesen, 2009, “Composite Competitiveness Indicators With Endogenous Versus Predetermined Weights: An Application to the World Economic Forum”, McColl School of Business, Queens University of Charlotte Discussion Paper Series No. 2009-02. 19

1.18 Daniele Archibugi, Francesco Crespi, Mario Denni and Andrea Filippetti, 2009, “The Technological Capabilities of Nations: A Survey of Composite Indicators”, Associazione Rossi Doria, QA, Volume 2009, Issue 2 (May). ............................................................................................20

1.19 Ard H.J. den Reijer, 2009, “The Dutch business cycle - A finite sample approximation of selected leading indicators”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis JBCMA, Volume 5, Issue 2, Pages: 89-110. ........................................................................20

1.20 Osama A. B. Hassan, 2008, “Assessing The Sustainability Of A Region In The Light Of Composite Indicators”, World Scientific Publishing Co. Pte. Ltd., Journal of Environmental Assessment Policy and Management, Volume 10, Issue 01, Pages: 51-65. ......................................21

1.21 Jonas Dovern and Christina Ziegler, 2008, “Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators under Real-Time Condition”, Kiel Working Paper No. 1397.22

1.22 Miroslav Klúcik and Ján Haluška, 2008, “Construction of composite leading indicator for the Slovak economy”, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi, Volume 55, November, Pages: 363-370. ..................................................................................................................22

1.23 Gomez-Limon Jose A. and Riesgo Laura, 2008, “Alternative Approaches On Constructing A Composite Indicator To Measure Agricultural Sustainability”, European Association of Agricultural Economists, 107th Seminar, January 30-February 1, 2008, Sevilla, Spain No. 6489. 23

1.24 Albu Lucian Liviu, 2008, “A Model to Estimate the Composite Index of Economic Activity in Romania – IEF-RO”, Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting, Volume 5, Issue 2, Pages: 44-50. ..................................................................23

1.25 Ronny Nilsson and Emmanuelle Guidetti, 2007, “Current Period Performance of OECD Composite Leading Indicators (CLIs): Revision analysis of CLIs for OECD Member countries”, OECD Statistics Working Papers No. 2007/1. ...................................................................................24

1.26 Panizza Andrea, 2007, “Composite and decomposable indicators for evaluating RIA systems in practice: proposals for discussion and testing”, University Library of Munich, Germany, MPRA Paper No. 13069. ....................................................................................................25

1.27 Hiroshi Yamada, Yuzo Honda and Yasuyoshi Tokutsu, 2007, “Report - An Evaluation of Japanese Leading Indicators”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis, Volume 3, Issue 2, Pages: 217-234.......................................................................................26

1.28 Andrew Sharpe and Anne-Marie Shaker, 2007, “Indicators of Labour Market Conditions in Canada”, Centre for the Study of Living Standards, CSLS Research Reports No. 2007-03.....26


1.29 Frédéric Gonand, Isabelle Joumard and Robert Price, 2007, “Public Spending Efficiency: Institutional Indicators in Primary and Secondary Education”, OECD, OECD Economics Department Working Papers No. 543.................................................................................................27

1.30 A. Saltelli, G. Munda and M. Nardo, 2006, “From Complexity to Multidimensionality. The Role of Composite Indicators for Advocacy of EU Reform”, Katholieke Universiteit Leuven, Review of Business and Economics, Volume LI, Issue 3, Pages: 221-235........................................28

1.31 Mehdi Mostaghimi, 2006, “Predicting Us 2001 Recession, Composite Leading Economic Indicators, Structural Change And Monetary Policy”, World Scientific Publishing, The Singapore Economic Review, Volume 51, Issue 03, Pages: 343-363.................................................28

1.32 Ronny Nilsson and Olivier Brunet, 2006, “Composite Leading Indicators for Major OECD Non-Member Economies: Brazil, China, India, Indonesia, Russian Federation, South Africa”, OECD Statistics Working Papers No. 2006/1. ...................................................................................29

1.33 Christian Gayer and Julien Genet, 2006, “Using factor models to construct composite indicators from BCS data - a comparison with European Commission confidence indicator”, Directorate General Economic and Monetary Affairs, European Commission, European Economy - Economic Papers No. 240. ................................................................................................30

1.34 Rowena Jacobs, Maria Goddard and Peter C Smith, 2006, “Public services: are composite measures a robust reflection of performance in the public sector?”, Centre for Health Economics, University of York, Working Papers No. 016cherp...........................................................................30

1.35 Ard den Reijer, 2006, “The Dutch business cycle: which indicators should we monitor?”, Netherlands Central Bank, Research Department, DNB Working Papers No. 100.......................31

1.36 Laurens Cherchye, Wim Moesen, Nicky Rogge, Tom Van Puyenbroeck, Michaela Saisana, A. Saltelli, R. Liska, S. Tarantola, 2006, “Creating Composite Indicators with DEA and Robustness Analysis: the case of the Technology Achievement Index”, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics , Public Economics Working Paper Series No. ces0613. .................................................................................32

1.37 Almas Heshmati and JongEun Oh, 2006, “Alternative Composite Lisbon Development Strategy Indices: A Comparison of EU, USA, Japan and Korea”, Cattaneo University (LIUC), The European Journal of Comparative Economics, Volume 3, Issue 2, Pages: 133-170. ..............33

1.38 Ronny Nilsson, 2006, “Composite Leading Indicators and Growth Cycles in Major OECD Non-Member Economies and recently new OECD Members Countries”, OECD Statistics Working Papers No. 2006/5. ................................................................................................................33

1.39 Christian Gayer, 2005, “Forecast Evaluation of European Commission Survey Indicators”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis JBCMA, Volume 2, Issue 2, Pages: 157-184...................................................................................................................................34

1.40 Christian Dreger and Christian Schumacher, 2005, “Out-of-sample Performance of Leading Indicators for the German Business Cycle - Single vs. Combined Forecasts”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis JBCMA,Volume 2,Issue 1,Pages: 71-88.......................................................................................................................................................35

1.41 Maria Antoinette Silgoner, 2005, “An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States, Oesterreichische Nationalbank (Austrian Central Bank) in its journal Monetary Policy and the Economy, 2005, Issue 3, Pages: 66-89....................................................................................36

1.42 M. Saisana, A. Saltelli and S. Tarantola, 2005, “Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators”, Journal of the Royal Statistical Society Series A, Volume 168, Issue 2, Pages: 307-323. ...................................................37


1.43 Michela Nardo, Michaela Saisana, Andrea Saltelli, Stefano Tarantola, Anders Hoffman and Enrico Giovannini, 2005, “Handbook on Constructing Composite Indicators: Methodology and User Guide”, OECD Statistics Working Papers No. 2005/3. ............................................................37

1.44 Aslihan Atabek, Evren Cosar and Saygin Sahinoz, 2005, “A New Composite Leading Indicator for Turkish Economic Activity”, M.E. Sharpe, Inc., Emerging Markets Finance and Trade, Volume 41, Issue 1, Pages: 45-64. ...........................................................................................38

1.45 Harm Bandholz, 2005, “New Composite Leading Indicators for Hungary and Poland”, Ifo Working Paper No. 3............................................................................................................................39

1.46 Jong-Eun Oh and Almas Heshmati, 2005, “Alternative Composite Lisbon Development Strategy Indices”, Institute for the Study of Labor (IZA), IZA Discussion Papers No. 1734........39

1.47 Laurens Cherchye, Knox Lovell, Wim Moesen and Tom Van Puyenbroeck, 2005, “One Market, One Number? A Composite Indicator Assessment of EU Internal Market Dynamics”, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Public Economics Working Paper Series No. ces0513......................................................................................................................40

1.48 Claudia Cicconi, 2005, “Building smooth indicators nearly free of end-of-sample revisions”, ISAE - Institute for Studies and Economic Analyses - (Rome, ITALY), ISAE Working Papers No. 49. ....................................................................................................................................................40

1.49 J. M. Binner, R. K. Bissoondeeal and A. W. Mullineux, 2005, “A composite leading indicator of the inflation cycle for the Euro area”, Taylor and Francis Journals, journal Applied Economics, Volume 37, Issue 11, Pages: 1257-1266...........................................................................41

1.50 Andrew Sharpe, 2004, “Literature Review of Frameworks for Macro-indicators”, Centre for the Study of Living Standards, CSLS Research Reports No. 2004-03.......................................41

1.51 Henk C. Kranendonk, Jan Bonenkamp and Johan P. Verbruggen, 2004, “A Leading Indicator for the Dutch Economy – Methodological and Empirical Revision of the CPB System”, CESifo Group Munich, CESifo Working Paper Series No. 1200.....................................................42

1.52 Michael Graff and Richard Etter, 2004, “Coincident and Leading Indicators of Manufacturing Industry Sales, Production, Orders and Inventories in Switzerland”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis, Volume 1, Issue 1, Pages: 109-132. 43

1.53 Mehdi Mostaghimi, 2004, “Monetary policy, composite leading economic indicators and predicting the 2001 recession”, John Wiley and Sons, Journal of Forecasting, Volume 23, Issue 7, Pages: 463-477.......................................................................................................................................44

1.54 Maximo Camacho, 2004, “Vector smooth transition regression models for US GDP and the composite index of leading indicators”, John Wiley and Sons, Journal of Forecasting, Volume 23, Issue 3, Pages: 173-196. ........................................................................................................................44

1.55 Roberto J. Tibana, 2003, “The Composite Indicator of Economic Activity in Mozambique (ICAE): Filling in the Knowledge Gaps to Enhance Public-Private Partnership (PPP)”, OECD Development Centre Working Papers No. 227. .................................................................................45

1.56 Francesco Battaglia and Livio Fenga, 2003, “Forecasting composite indicators with anticipated information: an application to the industrial production index”, Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 52, Issue 3, Pages: 279-290. ..................45

1.57 Michael Freudenberg, 2003, “Composite Indicators of Country Performance: A Critical Assessment”, OECD Science, Technology and Industry Working Papers No. 2003/16. ................46


1.58 Konstantin A. Kholodilin, 2003, “US composite economic indicator with nonlinear dynamics and the data subject to structural breaks”, Taylor and Francis Journals, Applied Economics Letters, Volume 10, Issue 6, Pages: 363-372. ..................................................................46

1.59 António Rua and Luís Catela Nunes, 2003, “Coincident and Leading Indicators for the Euro Area: A Frequency Band Approach”, Banco de Portugal, Economics and Research Department Working Papers No. w200307........................................................................................47

1.60 Zbigniew Matkowski, 2002, “Composite Indicators of Business Activity for Poland Based on Survey Data”, International Association of Economic Cycles, Review on Economic Cycles, Volume 4, Issue 1. .................................................................................................................................47

1.61 Konstantin Arkadievich Kholodilin, 2002, “Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator”, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES), Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) No. 2002027. ..........................................................................48

1.62 Giancarlo Bruno and Marco Malgarini, 2002, “An Indicator of Economic Sentiment for the Italian Economy”, ISAE - Institute for Studies and Economic Analyses, ISAE Working Papers No. 28. ....................................................................................................................................................48

1.63 Shin-ichi Fukuda and Takashi Onodera, 2001, “A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance?”, CIRJE, Faculty of Economics, University of Tokyo, CIRJE F-Series No. CIRJE-F-101. ...........................49

1.64 Filippo Altissimo, Domenico J. Marchetti and Gian Paolo Oneto, 2000, “The Italian Business Cycle; Coincident and Leading Indicators and Some Stylized Facts”, Bank of Italy, Economic Research Department, Temi di discussione (Economic working papers) No. 377. .......50

1.65 N.J. Nahuis , 2000, “Are Survey Indicators Useful for Monitoring Consumption Growth: Evidence from European Countries”, Netherlands Central Bank, Monetary and Economic Policy Department , MEB Series (discontinued) No. 2000-8........................................................................50

1.66 Gonzalo Camba-Mendez, George Kapetanios and Martin R. Weale, 1999, “The Forecasting Performance of the OECD Composite Leading Indicators for France, Germany, Italy and the UK”, National Institute of Economic and Social Research in , NIESR Discussion Papers No. 155. 51

1.67 Evan F. Koenig and Kenneth M. Emery, 1994, “Why the Composite Index of Leading Indicators Does Not Lead”, Western Economic Association International, Contemporary Economic Policy, Volume 12, Issue 1, Pages: 52-66...........................................................................51

1.68 Diebold Francis X. and Rudebusch Glenn D., 1989, “Scoring the Leading Indicators”, University of Chicago Press, Journal of Business, Volume 62, and Issue 3, Pages: 369-91............52

1.69 Wang George H. K., 1981, “A study of economic indicators in rail freight traffic cycles 1950-76”, Elsevier, Transportation Research Part B: Methodological, Volume 15, Issue 6, Pages: 391-405...................................................................................................................................................52


INTRODUCTION

A composite indicator may be defined as follows: ‘A composite indicator is formed

when individual indicators are compiled into a single index, on the basis of an

underlying model of the multi-dimensional concept that is being measured.’1

In other words, composite indicators are used to measure concepts which cannot be

captured by a single indicator. Ideally, it should be based on a theoretical framework

which allows individual indicators / variables to be selected, combined and weighted

in a manner which reflects the dimensions or structure of the phenomena being

measured.

Composite indicators are typically found in short-term economic analysis, for

example, the OECD developed a system of composite leading indicators (CLI) for its

member countries in the early 1980s based on the 'growth cycle' approach. However,

this ‘Selected Readings’ refers to a wide range of statistical fields in which composite

indicators are used, including as a measure of well-being, business climate,

competitiveness, innovative capacity, sustainable development, and inflation.

Apart from ‘Selected Readings’ on particular composite indicators, there are working

papers and articles on the construction of composite indicators, and on how to

measure their robustness or performance.

Contact point: GianLuigi Mazzi, "Responsible for Euro-indicators and statistical

methodology", Estat - D5 "Key Indicators for European Policies"

[email protected].

1 Source: OECD, 2004, “The OECD-JRC Handbook on Practices for Developing Composite Indicators”, paper presented at the OECD Committee on Statistics, 7-8 June 2004, OECD, Paris.

mailto:[email protected]


1 WORKING PAPERS AND ARTICLES

1.1 Castillo C. and Lorenzana T., 2010, “Evaluation of Business Scenarios By Means Of Composite Indicators”, International Association for Fuzzy-set Management and Economy (SIGEF), Fuzzy economic review, Volume XV, Issue 1, Pages: 3-20.

Assessing business scenarios with composite indicators allows the combination of

both quantitative and qualitative performance measures (PM). This paper presents a

methodology for constructing composite indicators by aggregating multiple PMs to

diagnose business performance. Assuming that measures are expressed in

heterogeneous units, these must be normalized in scales with a common base in order

to aggregate and combine data. This is achieved by modelling the opinions of experts

using the linguistic approach of the Fuzzy Sets Theory. In this way we obtain the sub-

indicators representing the excellence of each PM, quantified in a common scale.

These sub-indicators provide the basis for the construction of a composite indicator

whose value is determined by means of a fuzzy-rule-based system. This aggregation

procedure avoids compensation among sub-indicators and the possible redundancy of

the information they contain.

Full text available on-line at:

http://dialnet.unirioja.es/servlet/revista?codigo=8418

1.2 Jürgen Bierbaumer-Polly, 2010, “Composite Leading Indicator for the Austrian Economy. Methodology and "Real-time" Performance”, WIFO Working Papers No. 369.

This paper describes the methodologies used for constructing a composite leading

indicator for the Austrian economy (CLI-AT). First, a selection of those monthly

indicators which overall fare best in showing a "steady" leading behaviour with

respect to the Austrian business cycle was performed. The analysis was carried out by

means of statistical methods out of the time series domain as well as from the

frequency domain. Thirteen series have been finally classified as leading indicators.

Among them, business and consumer survey data form the most prevalent group.

http://dialnet.unirioja.es/servlet/revista?codigo=8418


Second, I construct the CLI-AT based on the de-trended, normalised and weighted

leading series. For the de-trending procedure I use the HP filter and the weights have

been obtained by means of principal components analysis. Further, idiosyncratic

elements in the CLI-AT have been removed along with checking the endpoint-bias

due to the HP filter smoothing procedure. I find that the "real-time" smoothed CLI-

AT does not exhibit severe phase-shifts compared to a full-sample estimate. Next, I

show that the CLI-AT provides a useful instrument for assessing the current and

likely future direction in the Austrian business cycle. Over the period 1988-2008, the

CLI-AT indicates cyclical turns with a "steady" lead in the majority of cases. Finally,

in using an out-of-sample forecasting exercise it is shown that the CLI-AT carries

important business cycle information and that its inclusion in a forecasting model can

increase the projection quality of the underlying reference series.


http://www.wifo.ac.at/wwa/jsp/index.jsp?fid=23923andid=39004andtypeid=8anddisplay_mode=2andpub_language=2andlanguage=2

1.3 Heike Belitz, Marius Clemens, Astrid Cullmann, Christian von Hirschhausen, Jens Schmidt-Ehmcke, Doreen Triebe and Petra Zloczysti, 2010, “Innovation Indicator 2009: Germany Has Still Some Catching Up to Do”, DIW Berlin, German Institute for Economic Research, journal Weekly Report, 2010,Issue 3,Pages: 13-19.

On behalf of the Deutsche Telekom Stiftung (Deutsche Telekom Foundation) and the

Bundesverband der Deutschen Industrien (Federation of German Industries) DIW

Berlin has investigated Germany's innovative capacity for the fifth time in an

international comparison. The survey evaluates the ability of countries to create and

transform knowledge into marketable products and services (i.e., innovations) using a

system of indicators that provides an overall composite indicator of innovative

capacity as well as a detailed profile of strengths and weaknesses. Of the seventeen

leading industrial nations investigated under the survey Germany only ranked 9th thus

remaining in the broad middle range. Relative to its most important competitors

Germany looses ground. The US, followed by Switzerland, Sweden, Finland, and

Denmark, headed up the list. Germany is particularly successful in its ability to

network key participants in the innovation process as well as in international markets

of high-technology sectors like mechanical engineering, chemical industry, vehicle

http://www.wifo.ac.at/wwa/jsp/index.jsp?fid=23923&id=39004&typeid=8&display_mode=2&pub_language=2&language=2

http://www.wifo.ac.at/wwa/jsp/index.jsp?fid=23923&id=39004&typeid=8&display_mode=2&pub_language=2&language=2


manufacturing and medical instruments. Deficiencies in Germany's education and in

the financing conditions for innovation and the founding of new companies, plus the

regulation of product markets remain the country's greatest innovation system

weaknesses.


http://www.diw.de/documents/publikationen/73/diw_01.c.346060.de/diw_wr_2010-03.pdf

1.4 Grupp Hariolf and Schubert Torben, 2010, “Review and new evidence on composite innovation indicators for evaluating national performance”, Elsevier Research Policy, Volume 39, Issue 1, Pages: 67-78.

The purpose of this contribution is to present a survey of the recent developments in

constructing composite science and technology (SandT) indicators on a national level

as well as new evidence of the variability of such SandT indicators which opens the

gateway to "country-tuning". It has become standard practice to combine several

indicators for science, technology, and innovation to form composite numbers.

Especially in the light of this variability, two questions arise. Firstly, are the results

(especially rankings) stable with respect to weights? Secondly, is there hope to define

"economically" reasonable weights? In order to provide answers to these questions,

we use data from the European Innovation Scoreboard 2005 (EIS 2005) to exemplify

our reasoning. Concerning the first question, we give genuine evidence on the

existence of immense variability, possibly invalidating the results. Further, we also

show that even existing and well-accepted methods, like equal weighting, Benefit of

the Doubt weighting (BoD) and principal component analysis weighting (PCA) may

lead to drastically differing results. Concerning the second question we will

demonstrate that by each composite indicator weighting a set of shadow prices is

implied expressing one indicator in terms of another. Whether the weights are

sensible should be evaluated on the basis of these shadow prices. It turns out that

those implied by EIS 2005 contain strange peculiarities. After that we plead for more

care in constructing composite indicators. Especially weights should be chosen on the

basis of shadow prices, rather than, say, by equal weighting or other automatic

methods. Lastly, we discuss the merit of composite indicators and argue that they

have a valuable communication and competition function, but they should be




accompanied by multidimensional representations, which provide the basis for the

construction of policy measures.


http://www.sciencedirect.com/science/article/B6V77-4XTP2N2-1/2/af00255305bc78f503de612d59ef0602

1.5 Laura Trinchera and Giorgio Russolillo, 2010, “On the use of Structural Equation Models and PLS Path Modeling to build composite indicators”, Macerata University, Department of Studies on Economic Development (DiSSE) Working Papers No. 30-2010.

Nowadays there is a pre-eminent need to measure very complex phenomena like

poverty, progress, well-being, etc. As is well known, the main feature of a composite

indicator is that it summarizes complex and multidimensional issues. Thanks to its

features, Structural Equation Modeling seems to be a useful tool for building systems

of composite indicators. Among the several methods that have been developed to

estimate Structural Equation Models we focus on the PLS Path Modeling approach

(PLS-PM), because of the key role that estimation of the latent variables (i.e. the

composite indicators) plays in the estimation process. In this work, first we present

Structural Equation Models and PLS-PM. Then we provide a suite of statistical

methodologies for handling categorical indicators in PLS-PM. In particular, in order

to take categorical indicators into account, we propose to use a modified version of

the PLS-PM algorithm recently presented by Russolillo [2009]. This new approach

provides a quantification of the categorical indicators in such a way that the weight of

each quantified indicator is coherent with the explicative ability of the corresponding

categorical indicator. To conclude, an application involving data taken from a paper

by Russet [1964] will be presented.


http://www.unimc.it/sviluppoeconomico/wpaper/wpaper00030/filePaper

1.6 Mohamed Daly Sfia, 2010, “A Composite Leading Indicator of Tunisian Inflation”, William Davidson Institute, University of Michigan, William Davidson Institute Working Papers Series No. wp980.



http://www.unimc.it/sviluppoeconomico/wpaper/wpaper00030/filePaper


This paper investigates the possibility of constructing a composite leading indicator

(CLI) of Tunisian inflation. For doing so, partial information about future inflation

rate provided by a number of basic series is analyzed first. Based on the correlation

analysis, a few of these basic series are chosen for construction of composite

indicator. Empirical results show that the deviation from long�term trend of two

monetary aggregates (M1 and M3), short�term interest rate (TMM), real effective

exchange rate and crude petroleum production, are important leading indicators for

inflation rate in Tunisia. Accordingly, based on monthly data on these basic series,

one composite indicator is constructed and its performance is assessed by using

turning point analysis, granger causality tests, and impulse response functions. The

results indicate that our composite indicator is useful in anticipating changes in

inflation rates in Tunisia.


http://wdi.umich.edu/files/publications/workingpapers/wp980.pdf

1.7 Gómez-Limón José A. and Sanchez-Fernandez Gabriela, 2010, “Empirical evaluation of agricultural sustainability using composite indicators”, Elsevier, Ecological Economics, Volume 69, Issue 5, Pages: 1062-1075.

The aim of this study was to develop a practical methodology for evaluating the

sustainability of farms by means of composite indicators, and to apply it to two

agricultural systems, the rain-fed agriculture of the Castilla y León countryside and

the irrigated systems of the valley of the River Duero. We hope thus to operationalise

the concept of sustainability as an element to support the "governance" of this sector.

Our methodology is based on calculating 16 sustainability indicators that cover the

three components of the concept (economic, social and environmental), and their

subsequent aggregation into nine different types of sustainability indices. Our results

enable us first to demonstrate the advantages and disadvantages of the various

methods used to construct composite sustainability indicators, demonstrating the

usefulness of analysing several of these indicators in conjunction, in order to obtain

more robust results. They also enable us to visualise farm heterogeneity within a

single agricultural system with respect to sustainability as well as to analyse the

structural and decision-oriented variables that influence it. Such information could

help to improve current agricultural policies (such as income policy, agricultural

http://wdi.umich.edu/files/publications/workingpapers/wp980.pdf


structure policy and rural development policy), with the aim of improving the

sustainability of the sector.


http://www.sciencedirect.com/science/article/B6VDY-4Y0JYT6-3/2/20d1cb9fc93945c0380189cb75bd4c30

1.8 Hiroshi Yamada, Syuichi Nagata and Yuzo Honda, 2010, “A comparison of two alternative composite leading indicators for detecting Japanese business cycle turning points”, Taylor and Francis Journals, Applied Economics Letters, Volume 17, Issue 9, Pages: 875-879.

The Organisation for Economic Cooperation and Development (OECD) has

developed a system of Composite Leading Indicators (CLIs) for its member countries.

On the other hand, the Japanese government has released another CLI for detecting

the Japanese business cycle turning points. Both CLIs are widely used alternatives.

These two CLIs may provide different business forecasts. When different forecasts

occur, how can we interpret the discrepancies? This article tries to answer this

question by clarifying their relationships.


http://www.informaworld.com/smpp/content~db=all?content=10.1080/13504850802570384

1.9 Michael Graff, 2010, “Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland”, Taylor and Francis Journals, Applied Economics, Volume 42, Issue 21, Pages: 2759-2781.

This article presents a multi-sectoral composite indicator for the Swiss GDP growth

rate, targeting a lead of two quarters. The in-sample period ranges from 1991 to 2002

and 14 data points are reserved as out of sample to assess the forecasting

performance. The results appear promising, in terms of both phase and amplitude.

Comparisons with two other uni-sectoral composite leading indicators for the same

reference series-the traditional KOF (Konjunkturforschungsstelle) barometer as

published until March 2006 and a uni-sectoral composite indicator computed from the

same indicators as the multi-sectoral instrument-show that the new approach is

superior to the alternatives, which is due to both its broader information basis as well






as to the structure that is imposed by the multi-sectoral design. Yet, there are

pronounced differences regarding the accuracy of the sectoral forecasts, so that there

is scope for improvement.



1.10 Ferdinand Fichtner, Rasmus Rüffer and Bernd Schnatz, 2009, “Leading indicators in a globalised world”, European Central Bank, Working Paper Series No. 1125.

Using OECD composite leading indicators (CLI), we assess empirically whether the

ability of the country-specific CLIs to predict economic activity has diminished in

recent years, e.g. due to rapid advances in globalisation. Overall, we find evidence

that the CLI encompasses useful information for forecasting industrial production,

particularly over horizons of four to eight months ahead. The evidence is particularly

strong when taking cointegration relationships into account. At the same time, we find

indications that the forecast accuracy has declined over time for several countries.

Augmenting the country-specific CLI with a leading indicator of the external

environment and employing forecast combination techniques improves the forecast

performance for several economies. Over time, the increasing importance of

international dependencies is documented by relative performance gains of the

extended model for selected countries.


http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1125.pdf

1.11 Salvati Luca and Zitti Marco, 2009, “Substitutability and weighting of ecological and economic indicators: Exploring the importance of various components of a synthetic index”, Elsevier in its journal Ecological Economics, Volume 68, Issue 4, Pages: 1093-1099.

The establishment of an information system is meaningful to develop a dynamic

assessment of environmental quality and degradation. In this paper an original,

empirical framework called Factor Weighting Model (FWM) is proposed to integrate

different indicators into a composite index. The FWM is able to work with variables



http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1125.pdf


depicting different themes (e.g. climate, soil, landscape, demography, economy)

collected at various geographical and temporal scales. Three case studies carried out

at different spatial scales were considered as examples of FWM application to a

composite index of Land Degradation. It consists of several variables collected from

various data sources and available at different temporal and spatial scales, which are

aggregated into some thematic indicators. The FWM approach was applied separately

for each case study to the single variables transformed into sensitivity indicators

through standard procedures. A multiway factor analysis (MDA) was carried out to

explore over time the relationships among indicators. The importance of the

environmental indicators was estimated by attributing a percent weight to each of

them according to MDA outputs. Climate and soil dimensions account for the highest

weights in all the cases considered. These findings are in accordance with the results

obtained in previous studies. The implications of FWM in the assessment of

environmental quality are finally discussed.


http://www.sciencedirect.com/science/article/B6VDY-4T9B2WV-2/2/27a186e08185d92cecf31adb63a09d69

1.12 James E. Foster, Mark McGillivray and Suman Seth, 2009, “Rank Robustness of Composite Indices”, Queen Elizabeth House, University of Oxford, OPHI Working Papers No. ophiwp26.

Many common multidimensional indices take the form of a 'composite index' or a

weighted average of several dimension-specific achievements. Rankings arising from

such an index are dependent upon an initial weighting vector, and any given judgment

could, in principle, be reversed if an alternative weighting vector was employed. This

paper examines a variable-weight robustness criterion for composite indicators that

views a comparison as robust if the ranking is not reversed at any weight vector

within a given set. We characterize the resulting robustness relations for various sets

of weighting vectors and illustrate how they moderate the complete ordering

generated by the composite indicator. We propose a measure by which the robustness

of a given comparison may be gauged and illustrate its usefulness using data from the

Human Development Index. In particular, we show how some country rankings are

fully robust to changes in weights while others are quite fragile. We investigate the




prevalence of the different levels of robustness in theory and practice and offer insight

as to why certain datasets tend to have more robust comparisons.


http://www3.qeh.ox.ac.uk/pdf/ophiwp/OPHIwp26.pdf

1.13 Giuseppe Munda and Michela Nardo, 2009, “Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting”, Taylor and Francis, journal Applied Economics, Volume 41, Issue 12, Pages: 1513-1523.

Composite indicators (or indexes) are very common in economic and business

statistics for benchmarking the mutual and relative progress of countries in a variety

of policy domains such as industrial competitiveness, sustainable development,

globalization and innovation. The proliferation of the production of composite

indicators by all the major international organizations is a clear symptom of their

political importance and operational relevance in policy-making. As a consequence,

improvements in the way these indicators are constructed and used seem to be a very

important research issue from both the theoretical and operational points of view. This

article aims at contributing to the improvement of the overall quality of composite

indicators (or indexes) by looking at one of their technical weaknesses, that is, the

aggregation convention used for their construction. For this aim, we build upon

concepts coming from multi-criteria decision analysis, measurement theory and social

choice. We start from the analysis of the axiomatic system underlying the

mathematical modelling commonly used to construct composite indicators. Then a

different methodological framework, based on noncompensatory/nonlinear

aggregation rules, is developed. Main features of the proposed approach are: (i) the

axiomatic system is made completely explicit and (ii) the sources of technical

uncertainty and imprecise assessment are reduced to the minimum possible degree.



http://www3.qeh.ox.ac.uk/pdf/ophiwp/OPHIwp26.pdf




1.14 L. Clavel and C. Minodier, 2009, “A Monthly Indicator of the French Business Climate”, Institut National de la Statistique et des Etudes Economiques, , Documents de Travail de la DESE - Working Papers of the DESE No. g2009-02.

In France, the business tendency surveys conducted in all the important sectors of the

economy are key components in diagnosing the economic outlook. Over the years,

INSEE has gradually introduced business climate indicators for each business sector.

Such indicators summarise the data contained in the many balances of opinion

supplied by the surveys and enable to measure the economic situation each month. An

indicator of this kind has been lacking, however, for the economy as the whole. To fill

this gap and enrich the existing panel of business climate indicators we provide in this

paper the first composite indicator based on French business surveys covering all the

important economic sectors of the French economy. We chose the dynamic factor

analysis to deal with mixed and changing frequencies and time availability of the data.

Parameters are estimated by maximum likelihood based on the Kalman filter. Several

indicators can be estimated according to the type (sector-based business climate

indicators or elementary components) and the number of variables included in the

model. To validate our results and choose the best indicator, we defined three criteria:

real-time stability, predictive accuracy to forecast GDP growth and ability to

reproduce French business cycles. The new monthly synthetic indicator which passed

the tests best allows a clear and simple interpretation of all the business surveys and

can deliver each month an early and accurate quantitative message concerning the

current business climate in France. This indicator can also be used to improve GDP

growth forecast.


http://www.insee.fr/en/publications-et-services/docs_doc_travail/G2009-02.pdf

1.15 Laurens Cherchye, Willem Moesen, Nicky Rogge and Tom Van Puyenbroeck, 2009, “Constructing a knowledge economy composite indicator with imprecise data”, Katholieke Universiteit Leuven, Center for Economic Studies, Discussion papers No. ces09.15.

This paper focuses on the construction of a composite indicator for the knowledge

based economy using imprecise data. Specifically, for some indicators we only have

information on the bounds of the interval within which the true value is believed to

http://www.insee.fr/en/publications-et-services/docs_doc_travail/G2009-02.pdf


lie. The proposed approach is based on a recent offspring in the Data Envelopment

Analysis literature. Given the setting of evaluating countries, this paper discerns a

‘strong country in weak environment’ and ‘weak country in strong environment’

scenario resulting in respectively an upper and lower bound on countries’

performance. Accordingly, we derive a classification of ‘benchmark countries’,

‘potential benchmark countries’, and ‘countries open to improvement’.


http://www.econ.kuleuven.be/eng/ew/discussionpapers/Dps09/Dps0915.pdf

1.16 Luciana Crosilla, Solange Leproux, Marco Malgarini and Francesca Spinelli, 2009, “Factor based Composite Indicators for the Italian Economy”, ISAE Working Papers No. 116.

A factor based approach is often used to build Composite Indicators (CI) from

qualitative data stemming from Business and Consumers Survey (BCS). Bruno and

Malgarini (2002) and Gayer and Genet (2006) have used factor analysis to synthesize

the information contained in the balances of the various surveys Harmonized by the

EC (industry, consumers, retail, building and services). However, Marcellino (2006)

pointed out that the use of aggregate balance series could imply missing relevant

information contained in the surveys. For this reason, in this paper we consider

additional information stemming from the percentage of equal answers; moreover, we

also use more disaggregate data at the branch level (considering socio-economics

characteristics of the respondents for the consumers survey). More specifically, we

consider Main Industrial Groupings for the industry survey; small and large multiple

shops for the retail survey; building and civil engineering for the construction survey;

households and business services for the service survey. Variables to be included in

the analysis are pre selected prior to factor extraction on the basis of their

contemporaneous or leading/lagging correlation with sector-specific target series.

Three methods are then used to extract Composite Indicators, namely Static Principal

Component Analysis and Static and Dynamic Factor Analysis (Forni, Hallin, Lippi,

Reichlin, 2000, 2001). The various Composite Indicators obtained from the factor

based approach are then investigated against the traditional Confidence Indicators in

terms of performance with respect to the reference series. As alternative evaluation

criteria we use: a) the cross-correlation between the CI and the reference series; b) the

http://www.econ.kuleuven.be/eng/ew/discussionpapers/Dps09/Dps0915.pdf


directional coherence of movement with the targets; c) turning points analysis

(determined applying the Bry-Boschan method). Finally, from the whole set of data

stemming from ISAE business and consumers survey we extract aggregate Composite

Indicators for the whole Italian economy using the same methods and evaluation

criteria outlined above. Indicators calculated with Static Factor Analysis on aggregate

balances show the best performance in tracking the reference cycle, i.e. the rate of

growth of Italian GDP.


http://www.isae.it/Working_Papers/WP_116_2009_Malgarini.pdf

1.17 Harry P. Bowen and Wim Moesen, 2009, “Composite Competitiveness Indicators With Endogenous Versus Predetermined Weights: An Application to the World Economic Forum”, McColl School of Business, Queens University of Charlotte Discussion Paper Series No. 2009-02.

In its call for papers, one of the stated aims for this special issue of the

Competitiveness Review was research that dealt with “Methodological difficulties in

measuring competitiveness” and that would “evaluate the Growth Competitiveness

Index (GCI) by the World Economic Forum …”. This is exactly the purpose of this

paper. This paper addresses an important methodological issue concerning the

construction of a composite indicator, and in particular composite indicators of

national competitiveness; these indicators often serve as a benchmark for

policymakers and others to judge the relative success of their country. Most

competitiveness indicators aggregate primitive data using predetermined fixed weight

values that are applied uniformly to all countries. The use of fixed and uniform

weights may bias inferences of relative performance since it ignores that countries can

have different policy priorities or lack inherent capabilities on some dimensions. In

addition, since the particular weight values chosen are not likely to be universally

accepted, the credibility of any particular index is weakened. To address this issue,

this paper proposes a procedure that allows for endogenously determined country

specific weights that explicitly take account of a country’s own choices and

achievement across primitive dimensions. We then illustrate our procedure by

applying it to examine the widely cited Global Competitiveness Index developed by

the World Economic Forum. Our resulting Revealed Competitiveness Index uses

http://www.isae.it/Working_Papers/WP_116_2009_Malgarini.pdf


weights that allow that countries may choose different combinations of the underlying

dimensions but still achieve the same level of overall performance. In general, our

method will prove useful to those wishing to construct and compare indexes of

performance, while minimizing objections about the “importance” of the different

component dimensions that often arise when predetermined and uniformly applied

weights are used.


ftp://ftp.drivehq.com/msbftp/repec/pdfs/wpapers/msbwp2009-02.pdf

1.18 Daniele Archibugi, Francesco Crespi, Mario Denni and Andrea Filippetti, 2009, “The Technological Capabilities of Nations: A Survey of Composite Indicators”, Associazione Rossi Doria, QA, Volume 2009, Issue 2 (May).

Composite synthetic indicators of the technological capabilities of nations have been

used ever more frequently over the last few years, creating a sort of Olympic Games

medal table of the innovation race. Such measurement tools have been formulated at

the macroeconomic level by the European Commission, specialised 98 United

Nations Agencies, the World Bank, the World Economic Forum and individual

scholars. All these indicators are based on a variety of statistical sources in order to

capture the multidimensional nature of technological change. This article reviews

these various exercises, in particular) casts light on the explicit and implicit

assumptions on the nature of technological change; ii) discusses their pros and cons;

iii) examines the soundness of the results achieved. Finally, the relevance of synthetic

indicators of technological capabilities for policy makers, company strategies and

economic studies is discussed.


http://www.francoangeli.it/riviste/Scheda_Riviste.asp?IDArticolo=36179andTipo=Articolo%20PDF

1.19 Ard H.J. den Reijer, 2009, “The Dutch business cycle - A finite sample approximation of selected leading indicators”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis JBCMA, Volume 5, Issue 2, Pages: 89-110.

ftp://ftp.drivehq.com/msbftp/repec/pdfs/wpapers/msbwp2009-02.pdf

http://www.francoangeli.it/riviste/Scheda_Riviste.asp?IDArticolo=36179&Tipo=Articolo%20PDF

http://www.francoangeli.it/riviste/Scheda_Riviste.asp?IDArticolo=36179&Tipo=Articolo%20PDF


In this study we construct a business cycle indicator for the Netherlands. The

Christiano-Fitzgerald band-pass filter is employed to isolate the cycle using the

definition of business cycle frequencies as waves with lengths longer than 3 years and

shorter than 11 years. The coincident business cycle index is based on industrial

production, household consumption and staffing employment. These three variables

represent key macroeconomic developments, which are also analysed by both the

CEPR and NBER dating committees. The composite leading index consists of eleven

indicators representing different sectors in the economy: three financial series, four

business and consumer surveys and four real activity variables, of which two supply -

and two demand-related. The pseudo real-time performance of the composite

indicator is analyzed by the extent to which the indicator gets revised as more data

becomes available. Finally, the composite leading indicator is employed in a bivariate

Vector Autoregressive model to forecast GDP growth rates.


http://www.oecd-ilibrary.org/economics/the-dutch-business-cycle_jbcma-2009-5ks9zc0t7rg2

1.20 Osama A. B. Hassan, 2008, “Assessing The Sustainability Of A Region In The Light Of Composite Indicators”, World Scientific Publishing Co. Pte. Ltd., Journal of Environmental Assessment Policy and Management, Volume 10, Issue 01, Pages: 51-65.

In this paper, an attempt is made to present a method to assess the sustainable

development of a region. The method is based on adapting the multi-attribute utility

theory to the concept of "composite indicator" of the region under concern; a

composite indicator represents a weighted number of components or sub-indicators.

Comparisons are made with the standard method of ranking and the axiomatic multi-

criteria method. It is shown that the suggested method is more practical and feasible

as it allows the studying of the potential improvement that can be performed in order

to heighten the status of the sustainable development of a region, in the long and short

run. Finally, a tentative quantitative index to evaluate the sustainability of a region is

proposed.


http://www.worldscinet.com/index.html



http://www.worldscinet.com/index.html


1.21 Jonas Dovern and Christina Ziegler, 2008, “Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators under Real-Time Condition”, Kiel Working Paper No. 1397.

In this paper we analyze the power of various indicators to predict growth rates of

aggregate production using real-time data. In addition, we assess their ability to

predict turning points of the economy. We consider four groups of indicators: survey

data, composite indicators, real economic indicators, and financial data. Almost all

indicators are found to improve short-run growth forecasts whereas the results for

four-quarter-ahead growth forecasts and the prediction of recession probabilities in

general are mixed. We can confirm the result that an indicator suited to improve

growth forecasts does not necessarily help to produce more accurate recession

forecasts. Only composite leading indicators perform generally well in both

forecasting exercises.


http://www.ifw-members.ifw-kiel.de/publications/predicting-growth-rates-and-recessions-assessing-u-s-leading-indicators-under-real-time-conditions/kap1397.pdf

1.22 Miroslav Klúcik and Ján Haluška, 2008, “Construction of composite leading indicator for the Slovak economy”, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi, Volume 55, November, Pages: 363-370.

Cyclical performance of economies in a turbulent environment is forcing researchers

to search for early signals of turning points between the phases of slowdowns and

accelerations. The most appropriate tool to solve this problem is the composite

leading indicator (CLI), which is an aggregate index of several individual indicators

proved to be statistical relevant for analyzing and forecasting of significant macro-

economic indicators (reference series). The leading indicator provides qualitative

information of the most probable performance of a reference cycle (i.e. GDP,

Industrial production) with a significant lead-time of several months. INFOSTAT (the

research institution of the Statistical Office of the Slovak Republic) is about to create

and use its own periodically published composite leading indicator (now the only CLI

for Slovakia is published by OECD) as a source of new information about so far non-




investigated economic relations with the aim to improve the quality of short-term

forecasts.


http://anale.feaa.uaic.ro/anale/resurse/038_S01_Klucik_Halushka.pdf

1.23 Gomez-Limon Jose A. and Riesgo Laura, 2008, “Alternative Approaches On Constructing A Composite Indicator To Measure Agricultural Sustainability”, European Association of Agricultural Economists, 107th Seminar, January 30-February 1, 2008, Sevilla, Spain No. 6489.

The aim of this paper is to carry out a comparative analysis of alternative methods on

constructing composite indicators to measure global sustainability of the agricultural

sector. This comparison is implemented empirically on the irrigated agriculture of the

Duero basin (Spain) as a case study. For this purpose, this research uses a dataset of

indicators previously calculated for different farm-types and policy scenarios. The

results allow to establish a hierarchy of the policy scenarios on the basis of the level

of sustainability achieved. Furthermore, analyzing the heterogeneity of different

farms-types in each scenario, is also possible to determine the main features of the

most sustainable farms in each case. All this information is useful in order to support

agricultural policy design and its implementation, trying to increase the sustainability

of this sector.


http://ageconsearch.umn.edu/bitstream/6489/2/cp08go17.pdf

1.24 Albu Lucian Liviu, 2008, “A Model to Estimate the Composite Index of Economic Activity in Romania – IEF-RO”, Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting, Volume 5, Issue 2, Pages: 44-50.

One of the most significant impediments for short-term forecasts is the frequency of

publishing GDP. At present, national institutes of statistics are publishing officially

registered GDP only quarterly. In our study, we tried to build a composite indicator

based on usually monthly data and to use it in order to obtain short-term forecasts for

economic activity at national level. This indicator could be useful taking into account

that actually there is no synthetic indicator to describe the short-run dynamics of

http://anale.feaa.uaic.ro/anale/resurse/038_S01_Klucik_Halushka.pdf

http://ageconsearch.umn.edu/bitstream/6489/2/cp08go17.pdf


economic activity. Thus, such an estimating model we are proposing for the

Romanian economy is coming from the last results in this field, especially from the

OECD methodology. Moreover, to validate the main hypotheses of the estimating

model for the composite indicator in the case of the Romanian economy we used the

quarterly data and, as benchmark indicator was considered the quarterly published

GDP. Using certain models based on composite indicators (leading indicators,

coincidence indicators, and post-cycle indicators), beside other models to analyse high

frequency time series and to obtain sort-term forecasts (such as principal component

method, so-called virtual monthly GDP method or various interpolating methods), it

can result in richer information for the business environment which in modern times

founds itself in an accelerated process of change.


http://www.ipe.ro/rjef/rjef2_08/rjef2_08_3.pdf

1.25 Ronny Nilsson and Emmanuelle Guidetti, 2007, “Current Period Performance of OECD Composite Leading Indicators (CLIs): Revision analysis of CLIs for OECD Member countries”, OECD Statistics Working Papers No. 2007/1.

This paper presents a comprehensive analysis of the current period performance of the

OECD composite leading indicators (CLIs) for 21 OECD Member countries and three

zone aggregates (OECD area, Euro area and Major Seven countries) for which CLIs

are available for a longer time period. The revisions analysis of OECD CLIs is similar

to those recently undertaken by the Organisation for a range of quantitative short-term

economic indicators. The aim of the current analysis on CLIs is to further evaluate the

quality of the indicator in order to: identify areas where their reliability could be

improved; and provide further information to users on their use for economic

analyses. The results show that first estimates of CLIs are revised frequently but the

size of revisions is rather small for most countries and almost neglectable for zone

aggregates and there is no evidence of bias. They also indicate that there is an

improvement in the reliability of the second estimates. The OECD CLI is, however,

designed to provide early signals of turning points (peaks and troughs) between

expansions and slowdowns of economic activity. It provides qualitative information

on short-term economic movements rather than quantitative measures. Therefore, the

http://www.ipe.ro/rjef/rjef2_08/rjef2_08_3.pdf


main message of CLI movements over time is the direction up or down rather than

levels. A simple measure which considers the direction is the sign of the movements.

The results show that for almost all the countries, around 90% of the time the sign of

the initial estimates of year-on-year growth rates and the 6 month rate of change are

the same as the ones published one month later. So the initial estimate can be

considered as a good indicator of whether economic activity will move up or down in

the near term future...


http://www.oecd-ilibrary.org/docserver/download/fulltext/5l4jbggd0828.pdf?expires=1308580390&id=id&accname=guest&checksum=E1995AAF1D26209FFAD676777783CE0A

1.26 Panizza Andrea, 2007, “Composite and decomposable indicators for evaluating RIA systems in practice: proposals for discussion and testing”, University Library of Munich, Germany, MPRA Paper No. 13069.

The spread in the adoption of RIA (regulatory impact analysis), sponsored by

international organisations, will hopefully result also in an increase of its usage, by

countries other than the few ones (mostly of Anglo-Saxon/common law tradition)

where it is established since long. This, in turn, would render factual comparison of

RIA national practices an exercise both meaningful and desirable, in particular for

eliciting specific areas and pathways for improvement. This paper proposes a first

attempt for the development of a statistical tool where basic measures and/or tests (i.e.

individual indicators) are organised and grouped in composite indices addressing

different dimensions within RIA. The latter can be variously combined, resulting also

in more general, synthetic indicators, preserving the components’ constituent

elements. Due to current limitations in information availability, weights for

aggregation are left undetermined in practice; the same reason impacts on selection of

elementary indicators and the shape of composites, so that appropriate methodologies

ought to be applied to get to a fully operational stage. A derived frame is also

proposed, limited to a monetary perspective on the overall performance of RIA

national systems, by means of a handful of key indicators which are less dependent on

issues of aggregation. The whole package should thus be considered as an input for

discussion, to be amended and eventually refined by testing for robustness and

stability, starting from the information which is being collected in the international





DIADEM database developed within the European Network for Better Regulation

project.


http://mpra.ub.uni-muenchen.de/13069/1/MPRA_paper_13069.pdf

1.27 Hiroshi Yamada, Yuzo Honda and Yasuyoshi Tokutsu, 2007, “Report - An Evaluation of Japanese Leading Indicators”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis, Volume 3, Issue 2, Pages: 217-234.

This paper evaluates the performances of Japanese leading indicators in predicting

business cycle turning points. We extract the business cycle component in leading

indicators using the frequency selective filter proposed by Baxter and King (1999),

and we try to clarify empirically whether or not the leading composite index and its

component series truly lead the business cycle turning point dates officially

determined by the Japanese government. We argue that if we utilize the evaluated

properties of the component series, we may construct a composite leading indicator

which has some desirable properties as requested. As an illustration we provide one

such example.


http://www.oecd-ilibrary.org/economics/report-an-evaluation-of-japanese-leading-indicators_jbcma-v2007-art12-en

1.28 Andrew Sharpe and Anne-Marie Shaker, 2007, “Indicators of Labour Market Conditions in Canada”, Centre for the Study of Living Standards, CSLS Research Reports No. 2007-03.

The purpose of this study is to identify and assess the relevant measures and

indicators of labour market conditions, as concern unemployment, in the context of

current and future labour market and economic trends. It is also to verify whether and

how the importance of these measures has changed over time. There are three main

objectives for this report. The first is to ascertain to what degree the unemployment

rate is an adequate predictor of labour market conditions in the context of the

changing economy. Labour market conditions refer to the state of the labour market

and encompass different dimensions. The second is to assess the suitability of other

http://mpra.ub.uni-muenchen.de/13069/1/MPRA_paper_13069.pdf




labour market indicators as predictors of labour market conditions. The third is to

discuss the feasibility of aggregating relevant labour market indicators into a

composite indicator of labour market conditions that might be considered for use in

the design of government programs. This report concludes that the unemployment rate

is actually a good indicator of labour market conditions, although it is wise to

supplement it with additional indicators. The construction of composite indices does

not seem to provide much more information on labour market conditions than the

unemployment rate does.


http://www.csls.ca/reports/csls2007-03.pdf

1.29 Frédéric Gonand, Isabelle Joumard and Robert Price, 2007, “Public Spending Efficiency: Institutional Indicators in Primary and Secondary Education”, OECD, OECD Economics Department Working Papers No. 543.

This paper presents composite indicators of the institutional and policy characteristics

of educational systems, collated from the questionnaire responses of 26 Member

countries. These indicators provide an overview of the institutional framework in the

primary and secondary education sector and are constructed so as to be used for the

analysis of international differences in spending efficiency. The key features of the

institutional setting in the non-tertiary education sector are grouped under three

headings: i) the ability to prioritise and allocate resources efficiently (through

decentralisation and mechanisms matching resources to specific needs); ii) the

efficiency in managing spending at the local level (through outcome-focused policies

and managerial autonomy), and iii) the efficiency in service provision (through

benchmarking and user choice). For each country, an intermediate indicator is

computed for each of these six institutional properties. Composite indicators then

combine the six intermediate indicators of spending efficiency into a single, aggregate

measure. Results are presented and some of their implications are discussed. Overall,

the characteristics of the institutional framework in the non-tertiary public education

sector seem to be very favourable, compared to OECD average, in the United

Kingdom, Australia, Norway, Denmark and the Netherlands, whereas results are less

http://www.csls.ca/reports/csls2007-03.pdf


favourable for the Czech Republic, Greece, Luxembourg, Japan, Turkey, Hungary,

Belgium (French speaking community), Switzerland and Austria.


http://www.oecd-ilibrary.org/economics/public-spending-efficiency_315010655867

1.30 A. Saltelli, G. Munda and M. Nardo, 2006, “From Complexity to Multidimensionality. The Role of Composite Indicators for Advocacy of EU Reform”, Katholieke Universiteit Leuven, Review of Business and Economics, Volume LI, Issue 3, Pages: 221-235.

We explore to what extent composite indicators, capable of aggregating

multidimensional processes into simplified, stylised concepts, are up to the task of

underpinning the development of data based narratives for political advocacy of the

EU reform process.


http://www.econ.kuleuven.be/tem/jaargangen/2001-2010/2006/TEM%202006-3/TEM_2006-3_03_Saltelli.pdf

1.31 Mehdi Mostaghimi, 2006, “Predicting Us 2001 Recession, Composite Leading Economic Indicators, Structural Change And Monetary Policy”, World Scientific Publishing, The Singapore Economic Review, Volume 51, Issue 03, Pages: 343-363.

In an attempt to predict a peak in the US economy using a classical statistical decision

methodology and a Bayesian methodology and using the 1996 revised composite

leading economic indicators (CLI), it is learned that the Bayesian models have

generally outperformed the classical statistical ones and, among the Bayesian models,

the two using two and three consecutive CLI growth rates are superior in reliability

and in accuracy. These two models, however, failed to correctly predict the 2001

recession. In investigating the reasons behind their failures, we learned that: (1) if the

concurrent data for the economic structure of 1983 to 1999 are used for the prediction,

they have also been able to predict the 2001 recession correctly, but their overall

reliability is not as strong as before; (2) given the overwhelming weight of the

monetary policy tools in the CLI-1996 design and the combination of the economic

and political events in the year 2000, the less than expected effectiveness of the

http://www.oecd-ilibrary.org/economics/public-spending-efficiency_315010655867

http://www.econ.kuleuven.be/tem/jaargangen/2001-2010/2006/TEM 2006-3/TEM_2006-3_03_Saltelli.pdf

http://www.econ.kuleuven.be/tem/jaargangen/2001-2010/2006/TEM 2006-3/TEM_2006-3_03_Saltelli.pdf


monetary policy since 2001 has contributed to this failure; and (3) a possible

structural change in the US economy since 2000 has also contributed to this

prediction failure.


http://papers.ssrn.com/sol3/papers.cfm?abstract_id=992779

1.32 Ronny Nilsson and Olivier Brunet, 2006, “Composite Leading Indicators for Major OECD Non-Member Economies: Brazil, China, India, Indonesia, Russian Federation, South Africa”, OECD Statistics Working Papers No. 2006/1.

The OECD developed a System of Composite Leading indicators for its Member

countries in the early 1980's based on the 'growth cycle' approach. Today the OECD

compiles composite leading indicators (CLIs) for 23 of its 30 Member countries and it

is envisaged to expand country coverage to include all Member countries and the

major six OECD non-member economies (NMEs) monitored by the organization in

the OECD System of Composite Leading Indicators. The importance of the six major

NMEs was considered the first priority and a workshop with participants from the six

major NMEs was held at the OECD in Paris in April 2005 to discuss an initial OECD

selection of potential leading indicators for the six major NMEs and national

suggestions for alternative and/or additional potential leading indicators for

calculation of country specific composite leading indicators. The outcomes of this

meeting and follow-up activities undertaken by the OECD in co-operation with the

participating national agencies are reflected in the results presented in this final

version of the document. The OECD indicator system uses univariate analysis to

estimate trend and cycles individually for each component series and then a composite

indicator is obtained by aggregation of the resulting de-trended components. Today,

statistical techniques based on alternative univariate methods and multivariate

analysis are increasingly used in cyclical analysis and some of these techniques are

used in this study to supplement the current OECD approach in the selection of

leading components and the construction of composite indicators.


http://www.oecd.org/dataoecd/5/19/34933314.pdf

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=992779



1.33 Christian Gayer and Julien Genet, 2006, “Using factor models to construct composite indicators from BCS data - a comparison with European Commission confidence indicator”, Directorate General Economic and Monetary Affairs, European Commission, European Economy - Economic Papers No. 240.

In the framework of the Joint Harmonised EU Programme of Business and Consumer

Surveys (BCS) the European Commission publishes two different kinds of monthly

composite indicators: simple-average based so-called “Confidence Indicators” and the

factor-analytic “Business Climate Indicator”. The Confidence Indicators are

calculated for the five different sectors (industry, services, retail trade, building and

consumers) covered by the BCS for all Member States, the euro area and the EU as a

whole. The Business Climate Indicator is only calculated for the euro area on the

basis of data from the industry survey. Starting from this duality and taking into

account the recent rise in popularity of factor models in applied economics, the aim of

this paper is twofold: First, we explore the option of extending the use of factor-based

approaches to the non-industry sectors and to individual Member States.

Second, we investigate possible performance gains of factor models over the

Confidence Indicators currently computed by the European Commission in tracking

the business cycle.

For this purpose, we compare the Confidence Indicators with four different factor-

analytic methods to extract composite indicators from BCS data.


http://ec.europa.eu/economy_finance/publications/publication856_en.pdf

1.34 Rowena Jacobs, Maria Goddard and Peter C Smith, 2006, “Public services: are composite measures a robust reflection of performance in the public sector?”, Centre for Health Economics, University of York, Working Papers No. 016cherp.

A composite indicator is an aggregated index comprising individual performance

indicators. Composite indicators integrate a large amount of information in a format

that is easily understood and are therefore a valuable tool for conveying a summary

assessment of performance in priority areas. This research investigates the degree to

which composite measures are an appropriate metric for evaluating performance in

http://ec.europa.eu/economy_finance/publications/publication856_en.pdf


the public sector. Do they reflect accurately the performance of organisations? To

what degree are they influenced by the uncertainty surrounding underlying indicators

on which they are based? Are they robust and stable over time? The construction of

composite measures creates specific methodological challenges that make such

questions especially pertinent. We address these through a series of quantitative

analyses of panel data relating to healthcare (Star ratings of NHS acute Trusts) and

local government (Comprehensive Performance Assessment (CPA) ratings of

authorities) in England where composites have been widely used. The creation of a

composite comprises a number of important steps, each of which requires careful

judgment. These include the specification of the choice of indicators, the

transformation of measured performance on individual indicators, the specification of

a set of weights on individual indicators, and combining the indicators using

aggregation methods or decision rules. We use Monte Carlo simulations to examine

the robustness of performance judgments to these different technical choices. We

show the extent to which composites provide stable performance rankings of

organisations over time and assess whether variations are due to genuine performance

improvement or merely the result of random statistical variation. The analysis

suggests that the judgments that have to be made in the construction of the composite

can have a significant impact on the resulting score. Technical and analytical issues in

the design of composite indicators have important policy implications. We highlight

the issues which need to be considered in the construction of robust composite

indicators so that they can be designed in ways which will minimise the potential for

producing misleading performance information which may fail to deliver the expected

improvements or even induce unwanted side-effects.


http://www.york.ac.uk/media/che/documents/papers/researchpapers/CHE%20Research%20Paper%2016.pdf

1.35 Ard den Reijer, 2006, “The Dutch business cycle: which indicators should we monitor?”, Netherlands Central Bank, Research Department, DNB Working Papers No. 100.

In this study we construct a business cycle indicator for the Netherlands. The

Christiano-Fitzgerald band-pass filter is employed to isolate the cycle using the

http://www.york.ac.uk/media/che/documents/papers/researchpapers/CHE Research Paper 16.pdf

http://www.york.ac.uk/media/che/documents/papers/researchpapers/CHE Research Paper 16.pdf


definition of business cycle frequencies as waves with lengths longer than 3 years and

shorter than 11 years. The main advantage of band-pass filtering is the unambiguous

concept of a business cycle, to which the filtered approximation will eventually

converge as more and more observations become available.

The coincident business cycle index is based on industrial production, household

consumption and staffing employment. These three variables represent key

macroeconomic developments, which are also analysed by both the CEPR and NBER

dating committees. For the indicator to be useful in practice, a timely update and

therefore a limited publication delay is a crucial constraint.

The composite leading index consists of eleven indicators representing different

sectors in the economy: three financial series, four business and consumer surveys

and four real activity variables, of which two supply- and two demand-related.


http://www.dnb.nl/binaries/Working%20Paper%20No%20100-2006%20_%20versie3_tcm46-146757.pdf

1.36 Laurens Cherchye, Wim Moesen, Nicky Rogge, Tom Van Puyenbroeck, Michaela Saisana, A. Saltelli, R. Liska, S. Tarantola, 2006, “Creating Composite Indicators with DEA and Robustness Analysis: the case of the Technology Achievement Index”, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics , Public Economics Working Paper Series No. ces0613.

Composite indicators are regularly used for benchmarking countries’ performance,

but equally often stir controversies about the unavoidable subjectivity that is

connected with their construction. Data Envelopment Analysis helps to overcome

some key limitations, viz., the undesirable dependence of final results from the

preliminary normalization of sub-indicators, and, more cogently, from the subjective

nature of the weights used for aggregating. Still, subjective decisions remain, and

such modelling uncertainty propagates onto countries’ composite indicator values and

relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess

robustness of final results and to analyze how much each individual source of

uncertainty contributes to the output variance. The current paper reports on these

http://www.dnb.nl/binaries/Working Paper No 100-2006 _ versie3_tcm46-146757.pdf

http://www.dnb.nl/binaries/Working Paper No 100-2006 _ versie3_tcm46-146757.pdf


issues, using the Technology Achievement Index as an illustration factor is more

important in explaining the observed progress.


http://www.econ.kuleuven.ac.be/ew/academic/econover/Papers/DPS0603.pdf

1.37 Almas Heshmati and JongEun Oh, 2006, “Alternative Composite Lisbon Development Strategy Indices: A Comparison of EU, USA, Japan and Korea”, Cattaneo University (LIUC), The European Journal of Comparative Economics, Volume 3, Issue 2, Pages: 133-170.

This study addresses the measurement of two composite Lisbon strategy indices that

quantifies the level and patterns of development for ranking countries. The first index

is nonparametric labeled as Lisbon strategy index (LSI). It is composed of six

components: general economics, employment, innovation research, economic reform,

social cohesion and environment, each generated from a number of Lisbon indicators.

LSI by reducing the complexity of the set of indicators, it makes the ranking

procedures quite simple. The second and parametric index is based on principal

component analysis. Despite the difference in the ranking by the two indices, it is

shown that the United States outperformed most EU-member states. Our

investigations also show evidence of significant dynamic changes taking place, as the

countries of the Union struggle to achieve the Lisbon goals. The necessity of a real

reform agenda in several old and new members and candidate countries emerges from

our analysis. We briefly refer to two important European phenomena emerging from

our data analysis and discuss the possible lessons learned from the Korean

development strategy.


http://eaces.liuc.it/18242979200602/182429792006030201.pdf

1.38 Ronny Nilsson, 2006, “Composite Leading Indicators and Growth Cycles in Major OECD Non-Member Economies and recently new OECD Members Countries”, OECD Statistics Working Papers No. 2006/5.

The OECD developed a System of Composite Leading Indicators (CLIs) for its

Member Countries in the early 1980’s based on the ‘growth cycle’ approach and up to

2006 the Organisation compiled composite leading indicators for 23 of the 30


http://eaces.liuc.it/18242979200602/182429792006030201.pdf


Member countries. Country coverage has now been expanded to include recently new

OECD member countries (Korea, New Zealand, Czech Republic, Hungary, Poland

and Slovak Republic) and the major six OECD non-member economies (Brazil,

China, India, Indonesia, Russian Federation and South Africa) monitored by the

organization in the OECD System of Composite Leading Indicators. The expansion of

the OECD System of Composite Leading Indicators to include the new CLIs for the

six recently new OECD member countries has implications for the calculation of the

OECD total area and the OECD Europe area aggregates. In addition, the inclusion of

the new CLIs for all of above twelve countries opens the possibility to calculate new

area aggregates such as Major Asian economies, Eastern Europe including or

excluding the Russian Federation and a World proxy to give information on the

overall global development. The importance of such new regional or area aggregates

is of course very much dependent on the existence of different cyclical patterns

between these new aggregates and the established ones. However, the calculation of a

World proxy aggregate is important in itself in so far that it will represent global

development better than the OECD total area aggregate.



1.39 Christian Gayer, 2005, “Forecast Evaluation of European Commission Survey Indicators”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis JBCMA, Volume 2, Issue 2, Pages: 157-184.

The study examines the contribution of different survey indicators published by the

European Commission to forecasting overall economic activity in the euro area. It

entails a quantitative evaluation of the information content of seven composite

indicators with regard to GDP growth. A preliminary analysis looks at the stationarity

and correlation properties of the variables. Based on bivariate VAR-models and the

notion of forecast improvement, the methodological approach is two-fold: In a first

step, the focused relations are studied from an ex post perspective: Employing

standard and individual Granger-causality tests, an initial assessment of the mean

predictive content of the indicators is provided. On the basis of impulse response and

variance decomposition analyses, some more light can be shed on the temporal

component of the interrelations between the variables. A more informative assessment



of the leading indicators' forecast enhancing power is based on out of sample

predictive performance. In a second step, therefore, an explorative out of sample

scenario is investigated. Attention is turned to the validation and differentiation of the

ex post-results. Finally, it is examined to what extent the relations that proved reliable

in the explorative scenario could have been useful in individual real-time settings. The

study affirms a useful informative content of the indicators in general and finds

encouraging individual results in the forecast exercises. The forecast potential is

however limited to short horizons. Generally, the predictive information proves better

exploitable when using the indicators in annual differences. The Economic Sentiment

Indicator (ESI) is found to be most useful up to one or two quarters ahead, while the

retail confidence indicator does not help to improve GDP growth forecasts. Moreover,

the BCI index does not prove more informative than the industrial confidence

indicator. The remarkably good performance of the building confidence indicator,

being interpreted with caution due to signs of spuriousness, may point to the need for

further research to clarify the nature of the different confidence indicators.

Full text available on-line at: http://www.oecd-ilibrary.org/economics/forecast-evaluation-of-european-commission-survey-indicators_jbcma-v2005-art2-en 1.40 Christian Dreger and Christian Schumacher, 2005, “Out-of-sample

Performance of Leading Indicators for the German Business Cycle - Single vs. Combined Forecasts”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis JBCMA,Volume 2,Issue 1,Pages: 71-88.

In this paper the forecasting performance of popular leading indicators for the German

business cycle is investigated. Survey based indicators (ifo business climate, ZEW

index of economic sentiment) and composite leading indicators (Handelsblatt,

Frankfurter Allgemeine Zeitung, Commerzbank) are considered. The analysis points

to a significant relationship of the indicators to the business cycle within the sample

period, as measured by the direction of causality. But, their out-of-sample forecasts do

not improve the autoregressive benchmark. This result may be caused by structural

breaks in the out-of-sample period. As combinations of forecasts tend to be more

robust against such shifts, pooled forecasts are constructed using different methods of

aggregation, including linear combinations of forecasts and common factor models. In

http://www.oecd-ilibrary.org/economics/forecast-evaluation-of-european-commission-survey-indicators_jbcma-v2005-art2-en

http://www.oecd-ilibrary.org/economics/forecast-evaluation-of-european-commission-survey-indicators_jbcma-v2005-art2-en


contrast to the single indicator approach, the combined indicator forecasts are able to

beat the benchmark at each forecasting horizon. Therefore, the analysis points to the

usefulness of pooling information in order to get more reliable forecasts.


http://www.oecd-ilibrary.org/economics/out-of-sample-performance-of-leading-indicators-for-the-german-business-cycle_jbcma-2005-5km7v183qs0v

1.41 Maria Antoinette Silgoner, 2005, “An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States, Oesterreichische Nationalbank (Austrian Central Bank) in its journal Monetary Policy and the Economy, 2005, Issue 3, Pages: 66-89.

This contribution provides an overview of the most common short-term indicators of

economic development in the euro area. These indicators are useful when official data

are released with long time lags or if they are subject to major revisions. Indicators

based on surveys among businesses, households, financial market analysts or

forecasters have the advantage of providing detailed and timely information on

individual sectors on a monthly basis and largely without later revision. As an

additional instrument, composite indicators, which are calculated by combining a

variety of measures into a single indicator with the help of regression and factor

analysis, offer an attractive tool for drawing conclusions from different, often

divergent signals. Even the most reliable economic indicators, however, can only be

interpreted as constituent elements of comprehensive economic analysis. With regard

to the new EU Member States, coverage is found to be limited as yet. This study also

shows that the forecasting quality of the European Commission’s business and

consumer surveys for the new Member States is not as high as for the other EU

Member States. As the reliability of economic indicators increases as forecasting

institutions and respondents gain more experience, coverage of established indicators

should be extended early on to this group of countries, in particular as some of the

new Member States may soon join the euro area.


http://www.oenb.at/en/img/mop_2005_q3_analysis4_tcm16-34759.pdf



http://www.oenb.at/en/img/mop_2005_q3_analysis4_tcm16-34759.pdf


1.42 M. Saisana, A. Saltelli and S. Tarantola, 2005, “Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators”, Journal of the Royal Statistical Society Series A, Volume 168, Issue 2, Pages: 307-323.

Composite indicators are increasingly used for bench-marking countries'

performances. Yet doubts are often raised about the robustness of the resulting

countries' rankings and about the significance of the associated policy message. We

propose the use of uncertainty analysis and sensitivity analysis to gain useful insights

during the process of building composite indicators, including a contribution to the

indicators' definition of quality and an assessment of the reliability of countries'

rankings. We discuss to what extent the use of uncertainty and sensitivity analysis

may increase transparency or make policy inference more defensible by applying the

methodology to a known composite indicator: the United Nations’ technology

achievement index.


http://onlinelibrary.wiley.com/doi/10.1111/j.1467-985X.2005.00350.x/abstract;jsessionid=F7B9DAFC5275A37D72DC7BD6E53C5BAD.d02t01

1.43 Michela Nardo, Michaela Saisana, Andrea Saltelli, Stefano Tarantola, Anders Hoffman and Enrico Giovannini, 2005, “Handbook on Constructing Composite Indicators: Methodology and User Guide”, OECD Statistics Working Papers No. 2005/3.

This Handbook aims to provide a guide for constructing and using composite

indicators for policy makers, academics, the media and other interested parties. While

there are several types of composite indicators, this Handbook is concerned with those

which compare and rank country performance in areas such as industrial

competitiveness, sustainable development, globalisation and innovation. The

Handbook aims to contribute to a better understanding of the complexity of composite

indicators and to an improvement of the techniques currently used to build them. In

particular, it contains a set of technical guidelines that can help constructors of

composite indicators to improve the quality of their outputs. It has been prepared

jointly by the OECD (the Statistics Directorate and the Directorate for Science,

Technology and Industry) and the Applied Statistics and Econometrics Unit of the





Joint Research Centre of the European Commission in Ispra, Italy. Primary authors

from the JRC are Michela Nardo, Michaela Saisana, Andrea Saltelli and Stefano

Tarantola. Primary authors from the OECD are Anders Hoffmann and Enrico

Giovannini. Editorial assistance was provided by Candice Stevens, Gunseli Baygan

and Karsten Olsen. The research is partly funded by the European Commission,

Research Directorate, under the project KEI (Knowledge Economy Indicators),

Contract FP6 No. 502529. In the OECD context, the work has benefitted from a grant

from the Danish government. The views expressed are those of the authors and should

not be regarded as stating an official position of either the European Commission or

the OECD.


http://www.oecd.org/LongAbstract/0,2546,en_2649_34257_35231682_119684_1_1_1,00.html

1.44 Aslihan Atabek, Evren Cosar and Saygin Sahinoz, 2005, “A New Composite Leading Indicator for Turkish Economic Activity”, M.E. Sharpe, Inc., Emerging Markets Finance and Trade, Volume 41, Issue 1, Pages: 45-64.

The aim of this paper is to construct a composite leading indicator (CLI) for Turkish

economic activity that would crucially provide earlier signals of turning points

between economic expansions and slowdowns. First, for this analysis, the index of

industrial production is selected as an indicator for economic activity. Second, a group

of variables that perform well both in forecasting and in tracking cyclical

developments of economic activity is selected from a broad set of economic indicators

related to industrial production. While constructing the CLI, a growth cycle approach

is used. The resulting cyclical patterns of the series are obtained by eliminating

seasonal, irregular, and trend components via TRAMO/SEATS programs and

Hodrick-Prescott filter. The selection of the component series is based on theoretical

economic significance and their leading performance at cyclical turning points. From

the selected series, different CLIs are constructed, and that with the best performance

is chosen as the CLI for Turkish economic activity.





http://mesharpe.metapress.com/app/home/contribution.asp?referrer=parentandbackto=issue,4,6;journal,36,48;linkingpublicationresults,1:111024,1

1.45 Harm Bandholz, 2005, “New Composite Leading Indicators for Hungary and Poland”, Ifo Working Paper No. 3.

This paper presents new composite leading indicators for the two largest of the EU

accession countries, Poland and Hungary. Using linear and non-linear dynamic factor

models we find for both countries that a parsimonious specification, which combines

national business cycle indicators, series reflecting trade volumes and supranational

business expectations makes for the most reliable business cycle leaders. The

composite leading indicators significantly Granger-cause GDP growth rates, while the

estimated Markov-switching probabilities of being in a recessionary state agree well

with a priori determined cycle chronologies.


http://www.cesifo-group.de/pls/guest/download/Ifo%20Working%20Papers%20%28seit%202005%29/IfoWorkingPaper-3.pdf

1.46 Jong-Eun Oh and Almas Heshmati, 2005, “Alternative Composite Lisbon Development Strategy Indices”, Institute for the Study of Labor (IZA), IZA Discussion Papers No. 1734.

This study addresses the measurement of two composite Lisbon strategy indices that

quantifies the level and patterns of development for ranking countries. The first index

is nonparametric labeled as Lisbon strategy index (LSI). It is composed of six

components: general economics, employment, innovation research, economic reform,

social cohesion and environment, each generated from a number of Lisbon indicators.

LSI by reducing the complexity of the set of indicators, it makes the ranking

procedures quite simple. The second and parametric index is based on principal

component analysis. Despite the difference in the ranking by the two indices, it is

shown that the United States outperformed most EU-member states. Our

investigations also show evidence of significant dynamic changes taking place, as the

countries of the Union struggle to achieve the Lisbon goals. The necessity of a real

http://mesharpe.metapress.com/app/home/contribution.asp?referrer=parent&backto=issue,4,6;journal,36,48;linkingpublicationresults,1:111024,1

http://mesharpe.metapress.com/app/home/contribution.asp?referrer=parent&backto=issue,4,6;journal,36,48;linkingpublicationresults,1:111024,1

http://www.cesifo-group.de/pls/guest/download/Ifo Working Papers %28seit 2005%29/IfoWorkingPaper-3.pdf




reform agenda in several old and new members and candidate countries emerges from

our analysis.


ftp://repec.iza.org/RePEc/Discussionpaper/dp1734.pdf

1.47 Laurens Cherchye, Knox Lovell, Wim Moesen and Tom Van Puyenbroeck, 2005, “One Market, One Number? A Composite Indicator Assessment of EU Internal Market Dynamics”, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Public Economics Working Paper Series No. ces0513.

We consider the lack of consensus about an appropriate theoretical framework linking

sub-indicators as a defining characteristic of composite indicators. This intrinsic

feature implies uncertainties about the appropriate normalisation and aggregation of

the raw data. The two are related: index theory offers some valuable guidelines about

their connection. Yet these do not fully solve the basic problem of expert

disagreement. We embed such (residual) disagreement in the aggregation method

itself. Specifically, we apply an impartial benefit-of-the-doubt weighting procedure,

where weight restrictions incorporate the available information on experts’ opinions.

We apply this procedure to the dynamic performance assessment of EU Internal

Market effects, thereby highlighting its capacity to disaggregate member states’

observed performance shifts into changes relative to benchmarks and performance

changes of the benchmarks (i.e. catching up versus genuine progress). Our results

indicate that the latter factor is more important in explaining the observed progress.



1.48 Claudia Cicconi, 2005, “Building smooth indicators nearly free of end-of-sample revisions”, ISAE - Institute for Studies and Economic Analyses - (Rome, ITALY), ISAE Working Papers No. 49.

Aim of this paper is the construction of smooth indicator of the Italian industrial

production index providing reliable end-of-sample information. Traditional smooth

indicators are obtained using univariate filtering procedures based on symmetric or

asymmetric filters inducing serious revisions. Here, the smoothing is obtained by

ftp://repec.iza.org/RePEc/Discussionpaper/dp1734.pdf



exploiting the information embedded in the cross sectional dimension which allows to

use a very narrow window, reducing the need for revisions at the end of the sample.

As a by-product, we also obtained a smooth composite leading indicator of the

industrial sector, based on eleven selected leading sectors.


http://www.isae.it/Working_Papers/WP_Cicconi_49_2005.pdf

1.49 J. M. Binner, R. K. Bissoondeeal and A. W. Mullineux, 2005, “A composite leading indicator of the inflation cycle for the Euro area”, Taylor and Francis Journals, journal Applied Economics, Volume 37, Issue 11, Pages: 1257-1266.

We evaluate the performance of composite leading indicators of turning points of

inflation in the Euro area, constructed by combining the techniques of Fourier

analysis and Kalman filters with the National Bureau of Economic Research

methodology. In addition, the study compares the empirical performance of Euro

Simple Sum and Divisia monetary aggregates and provides a tentative answer to the

issue of whether or not the UK should join the Euro area. Our findings suggest that,

first, the cyclical pattern of the different composite leading indicators very closely

reflect that of the inflation cycle for the Euro area; second, the empirical performance

of the Euro Divisia is better than its Simple Sum counterpart and third, the UK is

better out of the Euro area.


http://www.informaworld.com/smpp/content~content=a714022468~db=all

1.50 Andrew Sharpe, 2004, “Literature Review of Frameworks for Macro-indicators”, Centre for the Study of Living Standards, CSLS Research Reports No. 2004-03.

There has been an explosion of interest in recent years in Canada and other countries

in macro-indicators and composite indexes of economic and social well-being. This

reflects growing recognition of the important role macro-indicators can play as a tool

for evaluating trends in and levels of economic and social development and for

assessing the impact of policy on well-being. This report provides a literature review

of conceptual/operational frameworks for the development of macro-indicators that

http://www.isae.it/Working_Papers/WP_Cicconi_49_2005.pdf



give an assessment of economic, labour market and social conditions or states of well-

being. The report provides an analysis of frameworks for macro-indicators by

discussing general framework issues; identifies and describes six specific frameworks

for macro-indicators which the author regards as particularly important or relevant,

and discusses the strengths and weaknesses of these sets of indicators/composite

indexes; and provides a description of an additional 31 sets of indicators and

composite indexes broken down into economic, social, economic/social, and labour

market areas. The report concludes that no existing framework currently includes all

important concepts and linkages and that it is unlikely that one ever will. As the

survey of the macro-indicators literature reveals, the development of a framework for

macro-indicators involves choices related to the domains of interest, the purpose for

which the indicator is designed, and the population to be covered, among others.

Choices or tradeoffs must be made and a balance struck between conceptual

sophistication and transparency and between complex linkages that could potentially

confuse the user and simplicity.


http://www.csls.ca/reports/LitRevMacro-indicators.pdf

1.51 Henk C. Kranendonk, Jan Bonenkamp and Johan P. Verbruggen, 2004, “A Leading Indicator for the Dutch Economy – Methodological and Empirical Revision of the CPB System”, CESifo Group Munich, CESifo Working Paper Series No. 1200.

Since 1990 the Netherlands Bureau for Economic Policy Analysis (CPB) uses a

leading indicator in preparing short-term forecasts for the Dutch economy. This paper

describes some recent methodological innovations as well as the current structure and

empirical results of the revised CPB leading indicator. Special attention is paid to the

role and significance of IFO data. The structure of the CPB leading indicator is

tailored to its use as a supplement to model-based projections, and thus has a unique

character in several respects. The system of the CPB leading indicator is composed of

ten separate composite indicators, seven for expenditure categories (demand) and

three for the main production sectors (supply). This system approach has important

advantages over the usual structure, in which the basis series are directly linked to a

single reference series. The revised system, which uses 25 different basic series,

http://www.csls.ca/reports/LitRevMacro-indicators.pdf


performs quite well in describing the economic cycle of GDP, in indicating the

upturns and downturns, and in telling the story behind the business cycle.


http://www.cesifo-group.de/pls/guestci/download/CESifo%20Working%20Papers%202004/CESifo%20Working%20Papers%20May%202004/cesifo1_wp1200.pdf

1.52 Michael Graff and Richard Etter, 2004, “Coincident and Leading Indicators of Manufacturing Industry Sales, Production, Orders and Inventories in Switzerland”, OECD, CERIT, Journal of Business Cycle Measurement and Analysis, Volume 1, Issue 1, Pages: 109-132.

The Swiss Institute for Business Cycle Research regularly conducts business tendency

surveys (BTS) amongst manufacturing firms. The information thus generated is

available with a publication lead to the official Swiss sales, production, order and

inventory statistics. It is shown that the survey data can be used to generate

reasonably precise estimates of the reference series with leads of at least one quarter.

Specifically, cross correlations of quarterly series are computed to screen the data for

pairs of highly correlated business tendency survey series and corresponding official

statistics. All pairs are identified where the maximum correlation shows up

simultaneously or with a lead of the survey based series and exceeds a given

threshold. We then determine suitable subsets of indicator series with predetermined

leads and compute their principal components. The resulting coincident as well as

leading composite indicators for the reference series are closely correlated with the

reference series from 1990–2000 and the correlations generally seem to hold for the

out of sample period from 2001 to the present. Hence, apart from early availability,

the survey data indeed comprises information on the Swiss economy's track in the

near and medium term future. While the data refers to Switzerland, the methodology

can readily be applied in other countries.


http://www.oecd-ilibrary.org/economics/coincident-and-leading-indicators-of-manufacturing-industry_jbcma-v2004-art7-en

http://www.cesifo-group.de/pls/guestci/download/CESifo Working Papers 2004/CESifo Working Papers May 2004/cesifo1_wp1200.pdf






1.53 Mehdi Mostaghimi, 2004, “Monetary policy, composite leading economic indicators and predicting the 2001 recession”, John Wiley and Sons, Journal of Forecasting, Volume 23, Issue 7, Pages: 463-477.

On 26 November 2001, the National Bureau of Economic Research announced that

the US economy had officially entered into a recession in March 2001. This decision

was a surprise and did not end all the conflicting opinions expressed by economists.

This matter was finally settled in July 2002 after a revision to the 2001 real gross

domestic product showed negative growth rates for its first three quarters. A series of

political and economic events in the years 2000-01 have increased the amount of

uncertainty in the state of the economy, which in turn has resulted in the production of

less reliable economic indicators and forecasts. This paper evaluates the performance

of two very reliable methodologies for predicting a downturn in the US economy

using composite leading economic indicators (CLI) for the years 2000-01. It explores

the impact of the monetary policy on CLI and on the overall economy and shows how

the gradualness and uncertainty of this impact on the overall economy have affected

the forecasts of these methodologies. It suggests that the overexposure of the CLI to

the monetary policy tools and a strong, but less effective, expansionary money policy

have been the major factors in deteriorating the predictions of these methodologies.

To improve these forecasts, it has explored the inclusion of the CLI diffusion index as

a prior in the Bayesian methodology.


http://onlinelibrary.wiley.com/doi/10.1002/for.923/abstract

1.54 Maximo Camacho, 2004, “Vector smooth transition regression models for US GDP and the composite index of leading indicators”, John Wiley and Sons, Journal of Forecasting, Volume 23, Issue 3, Pages: 173-196.

In this paper, I extend to a multiple-equation context the linearity, model selection and

model adequacy tests recently proposed for univariate smooth transition regression

models. Using this result, I examine the nonlinear forecasting power of the

Conference Board composite index of leading indicators to predict both output growth

and the business-cycle phases of the US economy in real time.





1.55 Roberto J. Tibana, 2003, “The Composite Indicator of Economic Activity in Mozambique (ICAE): Filling in the Knowledge Gaps to Enhance Public-Private Partnership (PPP)”, OECD Development Centre Working Papers No. 227.

Peer review and public-private partnerships hinge on transparency. Transparency is

about commonly shared knowledge about the economy, its performance, and the way

policy influences it. The Composite Indicator of Economic Activity in Mozambique

(ICAE) is a tool of knowledge whose construction is based on an established

methodology in the construction of business cycles indicators amongst countries that

have such tradition. Its construction was primarily motivated by the need to enhance

constructive dialogue between domestic private and public sector agents in

Mozambique, by providing them with a common tool of knowledge about the long-

term and short- to medium-term performance and prospects of the economy, and how

this relates to policy. Due to its comparability with similar indicators that exist for the

country economies of key Mozambique foreign private and public sector partners, the

ICAE is also a potentially useful tool of constructive peer review, within the NEPAD

framework ...



1.56 Francesco Battaglia and Livio Fenga, 2003, “Forecasting composite indicators with anticipated information: an application to the industrial production index”, Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 52, Issue 3, Pages: 279-290.

Many economic and social phenomena are measured by composite indicators

computed as weighted averages of a set of elementary time series. Often data are

collected by means of large sample surveys, and processing takes a long time,

whereas the values of some elementary component series may be available a

considerable time before the others and may be used for forecasting the composite

index. This problem is addressed within the framework of prediction theory for




stochastic processes. A method is proposed for exploiting anticipated information to

minimize the mean-square forecast error, and for selecting the most useful elementary

series. An application to the Italian general industrial production index is illustrated,

which demonstrates that knowledge of anticipated values of some, or even just one,

component series may reduce the forecast error considerably.


http://onlinelibrary.wiley.com/doi/10.1111/1467-9876.00404/abstract

1.57 Michael Freudenberg, 2003, “Composite Indicators of Country Performance: A Critical Assessment”, OECD Science, Technology and Industry Working Papers No. 2003/16.

Composite indicators are synthetic indices of individual indicators and are

increasingly being used to rank countries in various performance and policy areas.

Using composites, countries have been compared with regard to their

competitiveness, innovative abilities, degree of globalisation and environmental

sustainability. Composite indicators are useful in their ability to integrate large

amounts of information into easily understood formats and are valued as a

communication and political tool. However, the construction of composites suffers

from many methodological difficulties, with the result that they can be misleading and

easily manipulated. This paper reviews the steps in constructing composite indicators

and their inherent weaknesses. A detailed statistical example is given in a case study.

The paper also offers suggestions on how to improve the transparency and use of

composite indicators for analytical and policy purposes.


http://www.oecd-ilibrary.org/science-and-technology/composite-indicators-of-country-performance_405566708255

1.58 Konstantin A. Kholodilin, 2003, “US composite economic indicator with nonlinear dynamics and the data subject to structural breaks”, Taylor and Francis Journals, Applied Economics Letters, Volume 10, Issue 6, Pages: 363-372.

Composite economic indicator is a very useful tool designed to trace and predict the

business cycle conditions. This paper studies possible extensions of this approach

http://onlinelibrary.wiley.com/doi/10.1111/1467-9876.00404/abstract




intended to cope with the potential data problems caused by various structural breaks

affecting both level and volatility of the component series. The structural shifts are

introduced in the composite economic indicator model via deterministic dummies

capturing breaks in the observed variables' intercepts and in the residual variances of

the specific factors. As an illustration the Post-Second World War US monthly

macroeconomic series are utilized for which different specifications of the single-

factor linear and regime-switching model are evaluated.



1.59 António Rua and Luís Catela Nunes, 2003, “Coincident and Leading Indicators for the Euro Area: A Frequency Band Approach”, Banco de Portugal, Economics and Research Department Working Papers No. w200307.

In the context of a common monetary policy, tracking euro area economic

developments becomes essential. The aim of this paper is to build monthly coincident

and leading composite indicators for the euro area business cycle. However, instead of

looking at the overall comovement between the variables as it is standard in the

literature, we show how one can resort to both time and frequency domain analysis to

achieve additional insight about their relationship. We find that, in general, the

lead/lag properties of economic indicators depend on the cycle’s periodicity.

Following a frequency band approach, we take advantage of this in the construction of

the coincident and leading composite indicators. The resulting indicators are analysed

and a comparison with other composite indicators proposed in the literature is made.


http://www.bportugal.pt/en-US/BdP%20Publications%20Research/WP200307.pdf

1.60 Zbigniew Matkowski, 2002, “Composite Indicators of Business Activity for Poland Based on Survey Data”, International Association of Economic Cycles, Review on Economic Cycles, Volume 4, Issue 1.

The paper presents a set of composite indicators of economic activity for Poland

based on qualitative data from business and consumer surveys. They refer to the

concept of economic sentiment indicator (ESI) used in EU countries, but some


http://www.bportugal.pt/en-US/BdP Publications Research/WP200307.pdf


alternative concepts proposed by the author are tested as well. Time series of the

indicators have been calculated for the period 1994-2001, using four alternative

formulas and two different sets of survey data. Statistical properties of the indicators

are analysed and business tendencies revealed by their evolution are compared with

the actual economic developments, as reflected by GDP and industrial production

index. The ultimate purpose is to assess the performance of such indicators in

business cycle analysis.


http://www.usc.es/~economet/cycles/cycles43.pdf

1.61 Konstantin Arkadievich Kholodilin, 2002, “Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator”, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES), Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) No. 2002027.

The analysis and prediction of the short-run economic dynamics, or the evolution of

the business cycle, often require a construction of the composite economic indicator

(CEI). This indicator may be endowed with nonlinear dynamics to take care of the

possible asymmetries between different phases of the business cycle. This paper

suggests using the smooth transition autoregression to model the CEI. The

performance of this model is compared to the already classical CEI with regime

switching. Both models turn out to produce statistically equally good results in terms

of forecasting the business cycle turning points.


http://sites-final.uclouvain.be/econ/DP/IRES/2002-27.pdf

1.62 Giancarlo Bruno and Marco Malgarini, 2002, “An Indicator of Economic Sentiment for the Italian Economy”, ISAE - Institute for Studies and Economic Analyses, ISAE Working Papers No. 28.

The long and sustained expansion of the nineties has generated, especially in the US,

widespread rumours about the “death of the cycle”. Nevertheless, towards the end of

the last decade, it became clear that fluctuations of economic activity were far from

being extinct. This has contributed greatly to a renewed interest among economists for

http://www.usc.es/~economet/cycles/cycles43.pdf

http://sites-final.uclouvain.be/econ/DP/IRES/2002-27.pdf


the elaboration of statistical indicators capable of tracking and, if possible,

anticipating the cyclical features of the economy. The aim of this paper is to build

such an aggregate composite indicator for the Italian Economy, based on the ISAE

surveys on households and those on the manufacturing, retail and construction sector.

The first step of the analysis consists in using a dynamic factor model to extract a

“common factor” from the different series of each survey, which may be interpreted

as a composite confidence indicator. We then evaluate, for each survey, its in-sample

and out-of sample properties, comparing them with those of the usual ISAE-EC

Confidence indicators. Finally, we use again the dynamic factor model to build, from

the sectoral Composite Indicator (CI), a Composite Aggregate Indicator (CAI) for the

Italian economy, and test its ability in tracking the cyclical features of Italian

aggregate GDP.


http://www.isae.it/Working_Papers/Bruno_Malgarini28.pdf

1.63 Shin-ichi Fukuda and Takashi Onodera, 2001, “A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance?”, CIRJE, Faculty of Economics, University of Tokyo, CIRJE F-Series No. CIRJE-F-101.

The purpose of this paper is to construct a new composite index of coincident

economic indicators in Japan and to demonstrate their usefulness in forecasting recent

short-run economic fluctuations. The method of construction is based on the single-

index dynamic factor model. Our two types of indexes are highly correlated with the

traditional composite index compiled by the EPA over business-cycle horizons.

However, standard leading indicators, which failed to forecast the traditional

composite index, make a satisfactory performance in forecasting our indexes in the

1990s. In addition, lagged values of our indexes help to improve the leading

indicators’ performance in forecasting the traditional composite index in the 1990s.

The result is noteworthy because a large number of research institutes made serious

errors in forecasting recent recessions in Japan.


http://www.cirje.e.u-tokyo.ac.jp/research/dp/2001/2001cf101.pdf

http://www.isae.it/Working_Papers/Bruno_Malgarini28.pdf

http://www.cirje.e.u-tokyo.ac.jp/research/dp/2001/2001cf101.pdf


1.64 Filippo Altissimo, Domenico J. Marchetti and Gian Paolo Oneto, 2000, “The Italian Business Cycle; Coincident and Leading Indicators and Some Stylized Facts”, Bank of Italy, Economic Research Department, Temi di discussione (Economic working papers) No. 377.

This paper analyses the business cycle properties of 183 time series relevant to the

Italian economy, including real, monetary and international variables. We propose

new monthly coincident and leading composite indicators for the Italian business

cycle; the leading indicator anticipates the turning points of the coincident indicator

on average by six months. On the methodological side, the study provides a scheme

for constructing cyclical indicators on a sound statistical basis through iterative steps,

combining the use of traditional NBER methods with that of more recent techniques

of cyclical analysis. A number of stylized facts of the Italian business cycle emerge.

Among them, money and financial variables are found to lead the cycle,

chronologically, by an average of between one year and sixteen months. There is also

strong evidence of synchronization of international cycles, with the US and UK cycles

leading the Italian cycle by two to three quarters. The main linking channel seems to

be trade, with Italian exports to EU countries leading the cycle by six months on

average.


http://www.bancaditalia.it/pubblicazioni/econo/temidi/td00/td377_00/td_377/tema_377_00.pdf

1.65 N.J. Nahuis , 2000, “Are Survey Indicators Useful for Monitoring Consumption Growth: Evidence from European Countries”, Netherlands Central Bank, Monetary and Economic Policy Department , MEB Series (discontinued) No. 2000-8.

This paper analyses the information content of two potential survey indicators for

consumption growth. Most short-term analyses only focus on consumer confidence,

which measures confidence of buyers of consumption goods. However, this paper

shows that this is optimal for only three of the eight countries in our sample. For the

other countries the retail sales indicator, a measure of confidence of sellers of

consumption goods, can improve the quality of short-term analyses substantially. For

the UK this indicator even outperforms consumer confidence. For the remaining four




countries we show that combining consumer sentiment and the retail sales indicator

into a composite indicator leads to optimal results.


http://www.dnb.nl/binaries/ms2000-08_tcm46-147296.pdf

1.66 Gonzalo Camba-Mendez, George Kapetanios and Martin R. Weale, 1999, “The Forecasting Performance of the OECD Composite Leading Indicators for France, Germany, Italy and the UK”, National Institute of Economic and Social Research in , NIESR Discussion Papers No. 155.

In this paper we present a methodology for evaluating the forecasting ability of

composite leading indicator variables of industrial economic activity. The new

methodology highlights the risks of variable selection in a VAR framework. The

methodology is applied to investigate the performance of the OECD composite

leading indicator in forecasting industrial production in four European countries.


http://www.niesr.ac.uk/pubs/dps/dp155.pdf

1.67 Evan F. Koenig and Kenneth M. Emery, 1994, “Why the Composite Index of Leading Indicators Does Not Lead”, Western Economic Association International, Contemporary Economic Policy, Volume 12, Issue 1, Pages: 52-66.

This paper assesses the real-time performance of the Commerce Department's

composite index of leading indicators. The authors find that the composite leading

index has failed to provide reliable advance warning of cyclical turning points. One

reason for this failure is that the leading index's transition from expansion to

contraction generally is not very sharp. Consequently, discerning real-time cyclical

peaks in the index is difficult. Transitions from contraction to expansion on average

are sharp. However, cyclical troughs in the leading index often precede cyclical

troughs in the economy by only a few months. Thus, even timely recognition of

troughs in the leading index fails to provide advance warning of turnarounds in the

general level of economic activity.

http://www.dnb.nl/binaries/ms2000-08_tcm46-147296.pdf

http://www.niesr.ac.uk/pubs/dps/dp155.pdf



http://onlinelibrary.wiley.com/doi/10.1111/j.1465-7287.1994.tb00412.x/abstract

1.68 Diebold Francis X. and Rudebusch Glenn D., 1989, “Scoring the Leading Indicators”, University of Chicago Press, Journal of Business, Volume 62, and Issue 3, Pages: 369-91.

The authors evaluate the ability of the composite index of leading indicators to predict

business cycle turning points. Formal probability-assessment scoring rules are applied

to turning-point probabilities generated from the leading index via a Bayesian

sequential probability recursion. These scoring rules enable rigorous and systematic

evaluation of leading indicator forecasts. The results are used to assess the merits of

forecasting with the composite leading index and to suggest possible improvements in

its construction.


http://www.jstor.org/

1.69 Wang George H. K., 1981, “A study of economic indicators in rail freight traffic cycles 1950-76”, Elsevier, Transportation Research Part B: Methodological, Volume 15, Issue 6, Pages: 391-405.

This paper examines the characteristics of rail freight traffic cycles from 1950 to

1976. Both the NBER's statistical indicator approach and time series approach are

used to identify the leading indicators of rail freight traffic cycles from a set of

leading economic indicators published by the Department of Commerce. The

concepts and empirical results obtained by these two procedures are compared and

contrasted. The interesting findings are: (1) the composite index of 12 leading

indicators performs very well as a qualitative and quantitative predictor and (2) the

empirical results obtained by the NBER approach are, in general, consistent with

those obtained by the time series approach.


http://www.sciencedirect.com/science/article/B6V99-466FHWX-25/2/67355f96d09900bd2529a663133f4e75

http://onlinelibrary.wiley.com/doi/10.1111/j.1465-7287.1994.tb00412.x/abstract

http://www.jstor.org/



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