1. Glossary and Handbook on Rapid Estimates
Presentation for the UN Workshop, 8 October 2014
By: Gian Luigi Mazzi

2. 1. Joint UNSD-Eurostat initiative 1.1 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

3. Three seminars taking place between 2009 and 2010
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

4. Five main actions launched as outcome of the seminars
1.1 Aim
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)

5. 1.2 Glossary on Rapid Estimates 1.2.1 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

6. 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

7. 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).

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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 …

15. 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

16. 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

17. 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

18. 1.3 Handbook on Rapid Estimates 1.3.1 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

19. Focusing also on communication and dissemination aspects
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

20. 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

21. 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

22. Part II: Statistical and econometric techniques for rapid estimates
Chapter 5: An overview of modelling techniques for rapid estimates
G.L. Mazzi and D. Sartore
Chapter 6: Variables selection approaches, the information set structure and various typologies of rapid estimates.
D. Ladiray

23. Chapter 7: Model selection, model specifications and various typologies of rapid estimates.
D. Ladiray, J. Mitchell

24. 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

25. 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

26. 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

27. PART V: Dissemination of rapid estimates
Chapter 16: Data Presentation Issues.
G. Singh (UNSD)

28. PART VI: Compilation guidelines
Chapter 17: Guidelines for rapid estimates.
Barcellan, Ladiray, Mazzi, Mitchell
Annexes
Glossary of rapid estimates (Barcellan, Hecq, Mazzi, Ruggeri)
Bibliography

29. Thank you for your attention.