Glossary and Handbook on Rapid Estimates

Presentation for the UN Workshop, 8 October 2014

By: Gian Luigi Mazzi

E-mail: gianluigi.mazzi@ec.europa.eu

1. Joint UNSD-Eurostat initiative1.1 Aim

• UNSD and Eurostat jointly undertaking initiatives in reaction to the global financial and economic crisis

• Further enhancement of infra-annual macro-economic statistics to better serve policy makers needs• Timely detect relevant changes • Higher reliability • Improved harmonisation and comparability across

countries and sectors 2

1.1 Aim

• Three seminars taking place between 2009 and 2010• Ottawa• Scheveningen• Moscow

• Large participation of institutions and countries all around the world • Productive and constructive exchange of views and

discussions• Very operational conclusions

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• Five main actions launched as outcome of the seminars • Glossary on rapid estimates (Eurostat)• Handbook on rapid estimates (Eurostat)• Handbook on cyclical composite indicators (Eurostat)• Handbook on opinion tendency surveys (ISTAT)• New data template (UNSD)

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1.1 Aim

1.2 Glossary on Rapid Estimates1.2.1 Introduction

• Lack of common terminology among countries and institutions when talking about rapid estimates • Same terminology used in very different contexts • Some communication and understanding problems

generated by this situation

• Need for a common vocabulary for various types of rapid estimates • Generally agreed • Based on a transparent and easily understandable

logical framework • Eurostat leading the preparation of the glossary on rapid

estimates 5

1.2.2 Structure of the glossary

• Glossary built up around 4 main questions

• Each question related to one or more axes of a theoretical hypercube

• Each axe has a number of modalities

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1.2.2 Structure of the glossary

• Main questions: • Who? Who makes the evaluation (1 axe).

• What? What is evaluated (2 axes).

• How? How is the evaluation done (3 axes).

• When? When is the evaluation done (2 axes).

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1.2.2 Structure of the glossary

Who makes the evaluation (1 axe)

Axe 1: The uniqueness of an official release vs. the potential multiplicity of evaluations

•Producer of rapid estimates may or may not be the same as the producer of regular releases of a given indicator

•Possible modalities• Statistical offices or members of the statistical system • Other governmental institutions • Private institutions 8

1.2.2 Structure of the glossary

What is evaluated (2 axes)

Axe 2. The target variable

•Possible modalities• Hard data• Soft data• Financial data• Unconventional data

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1.2.2 Structure of the glossary

What is evaluated (2 axes)

Axe 3. Some revisions in the estimate

•Theoretically speaking only data which is characterised by revisions can be the object of flash estimates or nowcasting but also data not subject to revisions can be forecasted

•Possible modalities• Data subsequently revised• Data is not revised

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1.2.2 Structure of the glossary

How is the evaluation done (3 axes)

Axe 4. The adherence to the regular production process

•Possible modalities • Fully adherent to the regular production process • Partially adherent to the regular production process• Different than the regular production process

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1.2.2 Structure of the glossary

How is the evaluation done (3 axes)

Axe 5. Information set• When estimating the target variable the information set on

which the estimation is based may or may not include the totality of the information

• In case of an incomplete coverage, statistical modelling used to fill the gaps • Defining a minimum acceptable coverage for each estimate

• Possible modalities• Availability of the full information set for the period under estimation• Incomplete observation set for the period under estimation

• Some variables could be observed only partially

• No available information for the period under estimation 12

1.2.2 Structure of the glossary

How is the evaluation done (3 axes)

Axe 6. Model/versus parameter uncertainty•Models used for rapid estimates differing for several reasons

• Known/unknown data • Techniques implying parameters estimation (uncertainty) vs. Simple

smoothing or adjustment techniques

•Possible modalities• Statistical models • Econometric models

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1.2.2 Structure of the glossary

When is the evaluation done (2 axes)

Axe 7. A proper reporting time• In defining rapid estimates the point in time at which

they are produced is an essential discriminant • Obviously the frequency of the target variable

influences the interpretation of various estimates

•Possible modalities• Estimates produced before the reference period• Estimates produced during the reference period• Estimates produced after the end of the reference period, but

not later than T+1/2• … 14

1.2.2 Structure of the glossary

When is the evaluation done (2 axes)

Axe 8: Stock and flow data/collecting and reference period

• When data are collected and how they are defined also affect the interpretation of various estimates

• A regular estimate for a flow variable cannot be produced before the end of the period while for a stock variable recorded at a given day or week of the reference period this would be possible

•Possible modalities• Flow• Stock

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1.2.3 Examples - Nowcasting

• Produced by a statistical authority or an institution outside a statistical system

• Target variable: hard data • Taking place for the reference period T during the period T

itself or right at the end• Making use of all available information becoming available

between T-1 and T until the estimation time• Using statistical and/or econometric models different from

the regular production process• Hard, soft, financial, unconventional data

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1.2.3 Examples – Flash estimates

• Produced by statistical institutions in charge of the regular production of the concerned indicator

• Target variable: hard data• Using an incomplete set of information exploiting as much

as possible all available hard data • Soft data can be used to fill some gaps

• Using as much as possible the same methodology as for regular estimates• Statistical techniques to deal with incomplete information set

• Released as timely as possible after the end of the reference period • Ideally not later than T+1/2

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1.3 Handbook on Rapid Estimates1.3.1 Aim

• Providing a comprehensive view of statistical and econometric techniques to produce rapid estimates• Consistently with the glossary classification

• Very didactical presentation of methods and techniques

• Advanced techniques also presented in detail • Mixed frequency models

• Internationally recognized authors

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1.3.1 Aim

• Facilitating the identification of best practices to produce various types of rapid estimates

• Focusing also on communication and dissemination aspects

• Targeting a large public• Official statisticians • Academics• Researchers• Students

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1.3.2 Structure and content of the handbook

Book I – Rapid Estimates: Conceptual and Practical Framework; Guidelines

Part I Generalities •  

Chapter 1: Introduction: objectives, definitions, costs and benefits of rapid estimates.

Eurostat and ECB/UN/other users

• Chapter 2: A system of rapid estimates: different products for different purposes.

R. Barcellan, G.L. Mazzi

• Chapter 3: Forecasting and nowcasting macroeconomic variables: a methodological overview

• D. Hendry, M. Weale

• Chapter 4: The trade-off between timeliness and reliability: the perspective of a statistical agency.

• S. Van Norden, E. Dubois, M. Weale

Part II: Statistical and econometric techniques for rapid estimates

Chapter 5: An overview of modelling techniques for rapid estimatesG.L. Mazzi and D. Sartore

Chapter 6: Variables selection approaches, the information set structure and various typologies of rapid estimates.D. Ladiray

• Chapter 7: Model selection, model specifications and various typologies of rapid estimates.

• D. Ladiray, J. Mitchell

PART III: Advanced modelling techniques

• Chapter 8: Mixed-frequency models and rapid estimates.

• M. Marcellino, Claudia Foroni• Chapter 9: Combining forecasting techniques

and rapid estimates• M. Marcellino• Chapter 10: An empirical investigation of

combining forecasting techniques• Charpin, Mazzi

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• Chapter 11: Combining forecasting techniques and density estimates

• Mitchell

• Chapter 12: Temporal disaggregation techniques • Mazzi, Proietti

• Chapter 13: Aggregated versus disaggregated approach for the construction of rapid estimates.

• Lui, J. Mitchell, Mazzi

PART IV: Some empirical results

•Chapter 14: Quality assessment of rapid estimates.•Ladiray, Mazzi, Sartore

•Chapter 15: Some empirical application of modelling techniques•Barcellan, Ladiray, Mazzi

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PART V: Dissemination of rapid estimates

• Chapter 16: Data Presentation Issues.• G. Singh (UNSD)

PART VI: Compilation guidelines

• Chapter 17: Guidelines for rapid estimates.• Barcellan, Ladiray, Mazzi, Mitchell

Annexes• Glossary of rapid estimates (Barcellan, Hecq,

Mazzi, Ruggeri)

• Bibliography

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Thank you for your attention.

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