Transcript PPT

Glossary and Handbook on Rapid
Estimates
Presentation for the UN Workshop, 8 October 2014
By: Gian Luigi Mazzi
E-mail: [email protected]
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 macroeconomic statistics to better serve policy makers
needs
• Timely detect relevant changes
• Higher reliability
• Improved harmonisation and comparability across
countries and sectors
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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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1.1 Aim
• Five main actions launched as outcome of the
seminars
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•
•
•
•
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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1.2 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 rapid5
estimates
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
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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
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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
• …
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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 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
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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
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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 estimates
G.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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