A dynamic factor model to assess the real time state of the Spanish industry using confidence...
Transcript of A dynamic factor model to assess the real time state of the Spanish industry using confidence...
A dynamic factor model to assess the real time A dynamic factor model to assess the real time state of the Spanish industry using confidence state of the Spanish industry using confidence
indicators indicators
Ángel CuevasResearch and Analysis Unit
Ministry of Industry, Energy and Tourism of Spain (MINETUR- prevMITYC)
SUBSECRETARÍA DE INDUSTRIA, ENERGÍA Y TURISMO
SECRETARÍA GENERAL TÉCNICA
Subdirección General de Estudios, Análisis y Planes de Actuación
EU workshop on business and consumer surveys (BCS)
Brussels, 15th-16th November 2012
BACKGROUNDInterest in improving the measurement of the
state of the industrial business cycle for purposes of:Anticipation of adverse situations.Evaluation and implementation of economic and
industrial policies.Improvement in databases of economic indicators.Advances in econometric methods for time series.Development of computer tools.Stock and Watson (1991, 2002), Gayer and Genet
(2006), Angelini et al. (2008) Camacho and Perez-Quirós (2009), Cuevas and Quilis (2011).
Objective: Multivariate modeling of a broad and representative set of monthly indicators of industrial activity, with the purpose of prediction, analysis and monitoring and forecasting of macroeconomic aggregates (industrial GVA).
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Inputs
Preprocessing
Dynamic factor model
{Treatment unbalanced
panel}
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Applications
Selection of indicators
• High frequency indicators (monthly).
• Must provide a synthetic measure of the Spanish industrial activity.
• They must be available promptly.• They must be correlated with the
reference series: Industrial Production Index (IPI)
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Selection of indicators
• Prove the correlation with the IPI: Cross-correlation with the growth
signal of SAC series. Cyclical Analysis: Butterworth
(band-pass) + classification of the turning points (Bry-Boschan).
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Selection of indicators
Variable SourceStart date
Release date
Unit
Industrial Confidence Indicator Ministry of Industry / EC 1990 01 t-2 days balance of repliesPMI Industry Markit Economics 1998 05 t+1 day balance of replies
Car Registrations General Directorate of Traffic 1990 01 t+1 day unitsElectricity Consumption Spanish Electricity Network 1990 01 t+1 day million Kw/h
Manufacture of cars Ministry of Industry 1994 01 t+25 days unitsManufacture of commercial and industrial
vehicles Ministry of Industry 1994 01 t+25 days units
Consumption of dieselPetroleum Products
Corporation1990 01 t+30 days thousand of metric tons
I ndustrial Production Index National Statistical Institute 1990 01 t+35 days volume index
Large Companies Sales. Industry Tax State Agency 1995 01 t+35 days deflated value indexTurnover Index in Industry National Statistical Institute 2002 01 t+50 days deflated value index
New Orders Index in Industry National Statistical Institute 2002 01 t+50 days deflated value index
Leading indicators are highlighted in yellow
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Leading indicators
Cross-correlation: y-o-y rates/differences
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Leading indicators
Cross-correlation: m-o-m rates/differences
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Leading indicators
Cross-correlation: q-o-q rates/differences(quarterly frequency)
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Leading indicators
Cyclical Analysis: ICI
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Leading indicators
Cyclical Analysis: Car registrations
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Leading indicators
Cyclical Analysis: PMI
Inputs
Preprocessing
Dynamic factor model
{Treatment unbalanced
panel}
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Applications
Preprocessing
• The series are adjusted for seasonal and calendar effects (if such effects are significant).• Logarithmically transformed.• Regular differences.
• The above variables are standardized.
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sac1t,i
sac1t,i
sact,isac
t,it,i x
xx)xlog()B1(z
sact,it,i x)B1(z (Soft)
Inputs
Preprocessing
Dynamic factor model
{Treatment unbalanced
panel}
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Applications
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ft
z1,t z2,t z3,t
u1,t u2,t u3,t
Static factor model
1(B) 2(B) 3(B)
e1,t e2,t e3,t
(B)
at
Common dynamic
Idiosyncratic dynamics
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Dynamic factor model: complete representation
et
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Factor model: dynamics
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Factor model: estimation (Nº. factors)
0 2 4 6 8 10 120
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1.5
2
2.5
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3.5
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SCREE PLOT
Number
Eig
enva
lue
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Dynamic factor model: estimation (f)
• The common factor and its standard deviation are estimated by Kalman filter, adjusting the dynamic factor model to state space representation.
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Dynamic factor model: loadings
Loadings Lead/ LagTurnover Index in Industry 0,90 0Industrial Production Index 0,83 0
New Orders Index in Industry 0,83 0Large Companies Sales. Industry 0,73 0
Consumption of diesel 0,71 0Manufacture of commercial and industrial
vehicles 0,43 0
PMI Industry 0,39 3Electricity Consumption 0,36 0
Manufacture of cars 0,36 0Industrial Confidence Indicator 0,31 3
Car Registrations 0,24 3
Inputs
Preprocessing
Dynamic factor model
{Treatment unbalanced
panel}
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Applications
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Estimation with an unbalanced panel
1 2 3 4 5 6 7 8123
......
......
......
......
T1
T2
Indicator
Obs
erva
tion
Longitudinal panel: initial estimate of the common factor
Cross-section panel
Dark grey: observedLight grey: non observed
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Estimation with an unbalanced panel
t 1 2 3 4 5 6 7 8 Static Dynamic123456789
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FactorI ndicator
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Estimation with an unbalanced panel
BALANCED PANEL: Using OLS (Stock-Watson)
t 1 2 3 4 5 6 7 8 Static Dynamic123456789
1011121314
FactorI ndicator
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Estimation with an unbalanced panel
BALANCED PANEL: Using Kalman Filter
t 1 2 3 4 5 6 7 8 Static Dynamic123456789
1011121314
I ndicator Factor
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Estimation with an unbalanced panel
REFINED BALANCED PANEL (KF). Repeat until convergence is achieved.
t 1 2 3 4 5 6 7 8 Static Dynamic123456789
1011121314
FactorI ndicator
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Estimation with an unbalanced panel
FINAL BALANCED PANEL AND FORECASTS: Using Kalman Filter.
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1011121314151617
FactorI ndicator
Inputs
Preprocessing
Dynamic factor model
{Treatment unbalanced
panel}
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Applications
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Industrial GVA and dynamic factor
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Industrial GVA and dynamic factor
Cross-correlation: y-o-y rates
Cross-correlation: q-o-q rates
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Forecasting and interpolation of industrial GVA
Benchmarking method: Chow-Lin, Fernández
Forecasting performance,
2003:Q1 – 2012:Q1 RMSE
ARIMA 2,755
DFM + Bench. 1,461
ISI (MEC) + Bench. 1,707
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Markov swithching model
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ConclusionsIt has developed a coincident indicator of Spanish
industrial activity, trying to exploit all possible information from various related monthly indicators.
The presence of leading indicators is critical in order to project the factor and anticipate the evolution of industrial activity in real time.
These leading indicators are ICI, PMI and car registrations and they have a lead of three months.
The methodology allows not only to estimate this factor, but also get individual predictions in a multivariate context of all the indicators included in the model.
With the estimated factor there are various options for use:
Perform a real time prediction of IGVATranslate its variations into probabilities of recession
This work can be extended in many directions: transfer functions, more sophisticated Markov switching models, etc.
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Angelini, E., Camba-Méndez, G., Giannone, D., Reichlin, L., Runstler, G (2008) “Short-term forecasts of Euro area GDP growth”. CEPR Discussion Paper n. 6746.
Camacho M, Pérez-Quirós G (2010) “Introducing the Euro-STING: Short Term Indicator of Euro Area Growth. Journal of Applied Econometrics.
Cuevas, A. & Quilis, E.M. (2011) “A factor analysis for the Spanish economy”. SERIEs Journal of the Spanish Economic Association.
Gayer, C. & Genet, J.(2006) “Using Factor Models to Construct Composite Indicators from BCS Data - A Comparison with European Commission Confidence Indicators”. Economic Papers N.240, European Commission.
Kim, C.-J. & Nelson, C.R. (1999) “State-Space Models with Regime Switching”, The MIT Press.
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References
Thanks for your attentionThanks for your attention
Ángel CuevasResearch and Analysis Unit
Ministry of Industry, Energy and Tourism of Spain (MINETUR)
SUBSECRETARÍA DE INDUSTRIA, ENERGÍA Y TURISMO
SECRETARÍA GENERAL TÉCNICA
Subdirección General de Estudios, Análisis y Planes de Actuación
EU workshop on business and consumer surveys (BCS)
Brussels, 15th-16th November 2012