Calling Recessions in Real Time By J.D. Hamilton Paper version : Econbrowser May 23 2010

1. Calling Recessions in Real TimeBy J.D. HamiltonPaper version : Econbrowser May 23 2010 Marc Wildi Marc.wildi@zhaw.ch

2. Emphasize of a True Challenge • “The paper stresses … actual real-time analysis, in which a researcher stakes his/her reputation on publicly using the model to generate out-of-sample, real-time predictions”. • Facing the `true’ future puts oneself in a situation which differs from `feigning to ignore withdrawn data’ (pseudo out of sample exercise). • USRI: http://www.idp.zhaw.ch/usri

3. Topics • Simplicity, robustness, continual changes • Statistical Revisions: Filter or Smooth? • Replicability • Data-Revisions • Mixed Frequency Approaches

4. Simplicity, Robustness, Continual Changes

5. Trade-Off • Abstract: “The paper … emphasizes the fundamental trade-off between parsimony – trying to keep the model as simple and robust as possible – and making full use of available information.”

6. Precision • One may ask: “as simple as possible” under which circumstance? • Optimization criterion

7. Shift of Perspective • Proposal: instead of emphasizing the (simplicity of the) model one could stress the (structure of the) optimization criterion.

8. One-Step Ahead Mean-Square Fit • `Closest fit’-principle is a black-box mechanism: • The criterion does anything to improve the in-sample fit • The user does not have any control on the outcome • Back door: parsimony

9. Customized Criteria • Could we account for additional or alternative user-relevant aspects of the estimation problem in the optimization criterion?

10. User Requirements • Expectations: • Reliability • Timeliness • Fitting user-needs `directly’ enhances performances: DFA • Confer an intrinsic out-of-sample quality to the customized criterion • The customized criterion addresses a fundamental uncertainty-principle

11. Conclusion (Section 6) • “Averaging the inference from alternative specifications or using Bayesian approaches to constrain more richly parameterized specifications are worth exploring.” • Customized criteria • “A particularly promising approach is to combine data of different frequencies”. • I’m more skeptical

12. Mixed-Frequency

13. Market Needs • Consumer eagerness for fresh news • As a response one observes a growing tendency to account for high-frequency (daily/weekly) data • ADS (Philadelphia Fed) • Camacho and Perez Quiros (EU) • Mix high-frequency with monthly and quarterly data

14. Questions • What additional information might we expect to appear in (and hopefully extract – in real time - from) disaggregated noisy `high-frequency’ data that is not already disclosed in monthly aggregates? • Up-date GDP on a `high-frequency’ scale? • How stable/robust are the relations?

15. ADS-Index

16. Revisions in the `Eye of the Storm’

17. Upshot • How much `reliability’ are consumers willing to trade against (alleged) `freshness’?

18. Data Revisions

19. Own Experience: USRI • USRI works with standardized log-returns of monthly data • In order to be effective, revisions of time series should affect standardized log-returns

20. USRI-Revisions due to Data-Update (Different Vintages)

21. Industrial Production • Frequently fast convergence • Sometimes larger revisions stretch over the whole history • Medium weight associated by filter

22. Manufacturing and trade sales • Frequently small revisions (fast convergence) • Sometimes larger revisions on whole history • Medium weight assigned by filter

23. Employment on non-agricultural payroll • Often fast convergence. Sometimes larger revisions but dynamics remain • `consistent’ (turning-points) • Important time series

24. Personal Income (Less Transfer Payments) • Large revisions (may affect dynamics) • Small weight attributed by the filter

25. Total civilian employment • Frequently unrevised (unfrequent revisions on whole history) • Most important time series (largest weight attributed by filter)

26. Filter or Smooth?

27. Smoothing • Smoothing the history of an Indicator hides/masks real-time performances • Smoothing cosmetics • USRI: WYSIWYG-design • Never calibrate a model on the history of a revised indicator (almost all of them) • Exception: USRI

28. Replicability

29. Replicability vs. Subjective Adjustments • Do index-providers discuss/adjust indicator values prior to release or is the release automated? • An important real-time indicator published by a national agency? • Do we have to attribute observed performances to improved statistics or to a clever economic staff relying on `insider’-knowledge (proprietary data)? • Can we (users) replicate an indicator? • Excel-replication of USRI • The Econbrowser-index can be replicated straightforwardly

30. Miscellaneous

31. Self-Fulfilling Prophecy • I firmly believe that it is possible to publish a `perfect‘ real-time indicator without affecting the course of the announced recession in any relevant way.

32. Continual Change of Economic Relations • We don‘t need necessarily fixed or stationary economic relations • What we need is a consistent/stable recession definition (by the NBER) • The USRI relies on `NBER-data‘ • Detecting peaks in Industrial Production, Manufacturing, Employment • Customized real-time filters are optimized to detect the relevant features (turning-points in these series) • As long as the NBER defines recessions this way, the USRI will be pretty `robust‘.

33. Uncertainty Principle • Reliability and timeliness are conflicting requirements • Address uncertainty-principle explicitly by customized criterion • USRI • MDFA

34. An Inconvenient Truth • The venerated maximum likelihood principle serves as a formal justification for a capricious black-box mechanism