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 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

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

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)

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

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

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).

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

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

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

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

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

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

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 …

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

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

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

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

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

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

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

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

Thank you for your attention.