RESEARCH WITH REAL-TIME MACROECONOMIC DATA

1. RESEARCH WITH REAL-TIME MACROECONOMIC DATA Dean CroushoreUniversity of RichmondJune 2005

2. Research Categories • Data Revisions • Forecasting • Monetary Policy • Macroeconomic Research • Financial Research

3. Data Revisions

4. Data Revisions • What Do Data Revisions Look Like? • Are They News or Noise? • Is the Government Using Information Efficiently? • Are Revisions Forecastable? • How Should We Model Data Revisions? • Key issue: are data revisions large enough economically to worry about?

5. Data Revisions • What Do Data Revisions Look Like? • First: Zellner (JASA 1958) • Short Term (example) • Long Term • What Do Different Types of Data Revisions Look Like? • Short run revisions based on additional source data • Benchmark revisions based on structural changes or updating base year (example)

7. Difference in log output across benchmark vintages from Croushore-Stark (JE 2001) Upward trend means later data revised downward relative to early data.

8. Data Revisions • Are Data Revisions News or Noise? • Data Revisions Contain News: Data are optimal forecasts, so revisions are orthogonal to early data; revisions are not forecastable • Data Revisions Reduce Noise: Data are measured with error, so revisions are orthogonal to final data; revisions are forecastable

9. Data Revisions • Are Data Revisions News or Noise? • Mankiw-Runkle-Shapiro (JME 1984): Money data revisions reduce noise • Mankiw-Shapiro (SCB 1986): GDP data revisions contain news • Mork (JBES 1987): GMM results show “final” NIPA data contain news; other vintages are inefficient and neither noise nor noise • UK: Patterson-Heravi (EJ 1991): revisions to most components of GDP reduce noise

10. Data Revisions • Is the Government Using Information Efficiently? • Theoretical Issue: Should the government report its sample information or project an unbiased estimate using extraneous information?

11. Data Revisions • Is the Government Using Information Efficiently? • Key Issue: What is the trade-off the government faces between timeliness and accuracy? • Zarnowitz (JB 1982): evaluates quality of different series • McNees (1989): found within-quarter estimate of GDP to be as accurate as estimate released 15 days after quarter end

12. Data Revisions • Findings of bias and inefficiency based on ex-post tests • UK: Garratt-Vahey (2003)

13. Data Revisions • Findings of bias and inefficiency of seasonally revised data • Kavajecz-Collins (REStat 1995) • Swanson-Ghysels-Callan (1999) • Key question: Are seasonal revisions predictable? Who cares if that is an artifact of construction?

14. Data Revisions • Key Issue: If early government data are projections, then state of business cycle may be related to later data revisions. • Dynan-Elmendorf (2001): GDP is misleading at turning points • Swanson-van Dijk (2004): volatility of revisions to industrial production and producer prices increases in recessions

15. Data Revisions • Are Revisions Forecastable? • Conrad-Corrado (JEDC 1979): use Kalman filter to improve government’s monthly data on retail sales • Aruoba (this conference)

16. Data Revisions • Are Revisions Forecastable? • Key Issue: can revisions be forecast in real-time (or just ex-post)? • Guerrero (IJF 1993): combines historical data with preliminary data on Mexican industrial production to get improved estimates of final data • Faust-Rogers-Wright (JMCB, forthcoming): Examines G-7 countries’ output forecasts; find Japan & U.K. output revisions forecastable in real time

17. Data Revisions • How Should We Model Data Revisions? • Howrey (1978) • Conrad-Corrado (1979) • UK: Holden-Peel (1982) • Harvey-McKenzie-Blake-Desai (1983) • UK: Patterson (EJ 1995) • UK: Kapetanios-Yates (2004)

18. Forecasting

19. Forecasting • Forecasts are only as good as the data behind them • Literature focuses on model development: trying to build a better forecasting model, especially comparing forecasts from a new model to other models or to forecasts made in real time

20. Forecasting • Does the fact that data are revised matter significantly (in an economic sense) for forecasts?

21. Forecasting • EXAMPLE: THE INDEX OF LEADING INDICATORS • Leading indicators: seem to predict recessions quite well. • But did they do so in real time? The evidence suggests skepticism. • Diebold and Rudebusch (1991) investigated the issue, using real-time data • Their conclusion: The leading indicators do not lead and they do not indicate! • The use of revised data gives a misleading picture of the forecasting ability of the leading indicators.

23. Forecasting • EXAMPLE: THE INDEX OF LEADING INDICATORS • Chart shows not much problem • But recession started in November 1973 • Subsequently, leading indicators were revised & ex-post they do much better

25. Forecasting • How Should Forecasts Be Made When Data Are Revised? • Key issue: temptation to cheat! • Try method; it doesn’t work; but that’s because of one outlier; dummy out that observation; the method works! • If data are not available, use a real-time proxy, don’t peak at future data • Cheating is inherent because you know the history already

26. Forecasting • Forecasting with Real-Time versus Latest-Available Data • Denton-Kuiper (REStat 1965): first to compare forecasts with real-time vs revised data • Cole (1969): data errors reduce forecast efficiency & may lead to biased forecasts • Trivellato-Rettore (JBES 1986): data errors in a simultaneous equations model affect everything: estimated coefficients and forecasts; but for small model of Italian economy, addition to forecast errors were not large

27. Forecasting • Forecasting with Real-Time versus Latest-Available Data • Faust-Rogers-Wright (JIE 2003): research showing forecastability of exchange rates depended on a particular vintage of data; other vintages show no forecastability • Summary: for forecasting, sometimes data vintage matters, other times it doesn’t

28. Forecasting • Levels versus Growth Rates • Howrey (JESM 1996): level forecasts of GNP more sensitive than growth forecasts; so policy should feed back on growth rates, not levels • Kozicki (JMacro 2002): choice of forecasting with real-time or latest-available data is important for variables with large level revisions

29. Forecasting • Model Selection and Specification • Swanson-White (REStat 1997): explores model selection • Robertson-Tallman (1998): real-time data affect model specification for industrial production but not for GDP • Harrison-Kapetanios-Yates (2002): it may be optimal to estimate a model without using most recent preliminary data • Summary: model choice is sometimes affected by data revisions

30. Forecasting • Evidence on Predictive Content of Variables • Croushore (NAJEF 2005): consumer confidence indicators have no predictive power in real time

31. Forecasting • Why Are Forecasts Affected by Data Revisions? • Change in data input into model • Change in estimated coefficients • Change in model itself (number of lags) • See experiments in Stark-Croushore (JMacro 2002)

32. Forecasting • What Do We Use as Actuals? • Answer: Depends on purpose • Best measures are probably latest-available data for “truth” (though perhaps not in fixed-weighting era) • But forecasters would not anticipate redefinitions and generally forecast to be consistent with government data methods (example: pre-chain-weighting period)

33. Forecasting • What Do We Use as Actuals? • Real-Time Data Set: many choices • first release (or second, or third) • four quarters later (or eight or twelve) • last benchmark (the last vintage before a benchmark revision) • latest available

34. Forecasting • Optimal Forecasting When Data Are Subject to Revision • Howrey (REStat 1978): adjusts for differing degrees of revision using Kalman filter; in forecasting, use recent data but filter it • Harvey-McKenzie-Blake-Desai (1983): use state-space methods with missing observations to account for irregular data revisions: large gain in forecast efficiency compared with ignoring data revisions

35. Forecasting • Optimal Forecasting When Data Are Subject to Revision • Howrey (REStat 1984): use of state-space models to improve forecasts of inventory investment yields little improvement • Patterson (IJF 2003): illustrates how to combine measurement process with data generation process to improve forecasts for income & consumption

36. Forecasting • Optimal Forecasting When Data Are Subject to Revision • Summary: There are sometimes gains to accounting for data revisions; but predictability of revisions (today for US data) is small relative to forecast error (mainly seasonal adjustment)

37. Forecasting • A Troublesome Issue • Specifying a process for data revisions • Some papers specify an AR process • But research on revisions suggests that benchmark revisions are not so easily characterized

38. Forecasting • Key Issue: What are the costs and benefits of dealing with real-time data issues versus other forecasting issues?

39. Monetary Policy

40. Monetary Policy: Data Revisions • How Much Does It Matter for Monetary Policy that Data Are Revised? • How Misleading Is Monetary Policy Analysis Based on Final Data Instead of Real-Time Data? • How Should Monetary Policymakers Handle Data Uncertainty? • How Do People React When Monetary Data Are Revised?

41. Monetary Policy: Data Revisions • How Much Does It Matter for Monetary Policy that Data Are Revised? • Example: Chairman Greenspan’s favorite inflation measure is the Personal Consumption Expenditures Price Index Excluding Food & Energy Prices (PCEPIXFE) • But it has been revised substantially!

42. Monetary Policy: Data Revisions • How Much Does It Matter for Monetary Policy that Data Are Revised? • Maravall-Pierce (Ea 1986): The Fed optimally signal extracts from the noise in money data, so data revisions would not significantly affect monetary policy

43. Monetary Policy: Data Revisions • How Misleading Is Monetary Policy Analysis Based on Final Data Instead of Real-Time Data? • Croushore-Evans (JME, forthcoming): Data revisions do not significantly affect measures of monetary policy shocks

44. Monetary Policy: Data Revisions • How Should Monetary Policymakers Handle Data Uncertainty? • Coenen-Levin-Wieland (2001): use money as an indicator when GDP data are uncertain • Bernanke-Boivin (2003): use factor model to incorporate much data; results do not depend on using real-time data instead of revised data

45. Monetary Policy: Data Revisions • How Should Monetary Policymakers Handle Data Uncertainty? • Giannone-Reichlin-Sala (2005): extract real-time information to determine a real shock and a nominal shock, which represent fundamental dynamics of US economy

46. Monetary Policy: Data Revisions • How Do People React When Monetary Data Are Revised? • Barro-Hercowitz (1980): Data revisions have no explanatory power for output

47. Monetary Policy: Analytical Revisions • What Happens When Economists or Policymakers Revise Conceptual Variables? • Orphanides (AER 2001, JME 2003) • Rudebusch (2001) • UK: Nelson-Nikolov (2001) • Lansing (2002) • Orphanides-Williams (BPEA 2002) • Cukierman-Lippi (2004) • Germany: Gerberding-Seitz-Worms (2004) • Germany: Döpke (2004) • Clark-Kozicki (2004) • Boivin (2004)

48. Monetary Policy: Analytical Revisions • What Happens When Economists or Policymakers Revise Conceptual Variables? • Key issue: end-of-sample inference for forward-looking concepts (filters) • Key issue: optimal model of evolution of analytical concepts

49. Monetary Policy: Analytical Revisions • If Monetary Policymakers Ignored Analytical Revisions, How Wrong Would They Be? • Real-Time Taylor Rules • Norway: Bernhardsen et al. (2004) • G7: Glück-Schleicher (2004) • Japan: Kamada (2004) • US: Kozicki (2004) • Switzerland: Kugler et al. (2004) • ECB: Mitchell (2004) • US: Orphanides-van Norden (1999) • Canada: Cayen-van Norden (2004)

50. Macroeconomic Research