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DATA REVISIONS AND THEIR CONSEQUENCES

DATA REVISIONS AND THEIR CONSEQUENCES. Introduction. Real Time Data sets Real-Time Data Set for Macroeconomists Philadelphia Fed + University of Richmond Need for good institutional support Club good: non-rival but excludable. Introduction. Data sets Unrestricted access:

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DATA REVISIONS AND THEIR CONSEQUENCES

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  1. DATA REVISIONS AND THEIR CONSEQUENCES

  2. Introduction • Real Time Data sets • Real-Time Data Set for Macroeconomists • Philadelphia Fed + University of Richmond • Need for good institutional support • Club good: non-rival but excludable

  3. Introduction • Data sets • Unrestricted access: • U.S.: Philadelphia Fed, St. Louis Fed, BEA • OECD • Bank of England (recently updated) • Restricted access: • EABCN • Fate unclear: • Canada • One-time research projects: • Many, most not continuously updated

  4. Introduction • Analysis of data revisions is not criticism of government statistical agencies! • May help agencies improve data production process • Revisions reflect limited resources devoted to data collection • Revised data usually superior to unrevised data (U.S. CPI vs. PCE price index)

  5. Introduction • Structure of data sets • The data matrix • Columns report vintages (dates on which data series are observed) • Rows report dates for which economic activity is measured • Moving across rows shows revisions • Main diagonal shows initial releases • Huge jumps in numbers indicate benchmark revisions with base year changes

  6. REAL OUTPUT Vintage: 11/65 2/66 5/66 . . . 11/07 2/08 Date 47Q1 306.4 306.4 306.4 . . . 1570.5 1570.5 47Q2 309.0 309.0 309.0 . . . 1568.7 1568.7 47Q3 309.6 309.6 309.6 . . . 1568.0 1568.0 . . . . . . . . . . . . . . . . . . . . . 65Q3 609.1 613.0 613.0 . . . 3214.1 3214.1 65Q4 NA 621.7 624.4 . . . 3291.8 3291.8 66Q1 NA NA 633.8 . . . 3372.3 3372.3 . . . . . . . . . . . . . . . . . . . . . 07Q1 NA NA NA . . . 11412.6 11412.6 07Q2 NA NA NA . . . 11520.1 11520.1 07Q3 NA NA NA . . . 11630.7 11658.9 07Q4 NA NA NA . . . NA 11677.4

  7. Data Revisions

  8. 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?

  9. Data Revisions • What Do Data Revisions Look Like? • Short Term (example) • Long Term (example) • 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

  10. Data Revisions • Are Data Revisions News or Noise? • Data Revisions Add 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

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

  12. 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?

  13. Data Revisions • Is the Government Using Information Efficiently? • Key Issue: What is the trade-off the government faces between timeliness and accuracy? • Zarnowitz (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

  14. Data Revisions • Findings of bias and inefficiency of seasonally revised data • Kavajecz-Collins (1995) • Swanson-Ghysels-Callan (1999) • Revisions to seasonals may be larger than revisions to NSA data: Fixler-Grimm-Lee (2003) • Key question: Are seasonal revisions predictable? Who cares if that is an artifact of construction?

  15. 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

  16. Data Revisions • Are Revisions Forecastable? • Conrad-Corrado (1979): use Kalman filter to improve government’s monthly data on retail sales • Aruoba (2008): revisions to many U.S. variables are forecastable

  17. Data Revisions • Are Revisions Forecastable? • Key Issue: can revisions be forecast in real-time (or just ex-post)? • Guerrero (1993): combines historical data with preliminary data on Mexican industrial production to get improved estimates of final data • Faust-Rogers-Wright (2005): Examines G-7 countries’ output forecasts; find Japan & U.K. output revisions forecastable in real time

  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 • Details: Croushore (2006) Handbook of Economic Forecasting

  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.

  22. 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

  23. 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 (2002)

  24. 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; 2013 capitalization of R&D)

  25. 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) • Date of annual revision (July for U.S. data) • last benchmark (the last vintage before a benchmark revision) • latest available

  26. 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

  27. Forecasting • Forecasting with Real-Time versus Latest-Available Data • Faust-Rogers-Wright (2003): research showing forecastability of exchange rates depended on a particular vintage of data; other vintages show no forecastability • Molodtsova (2007): combining real-time data with Taylor rule allows predictability of exchange rate • Moldtsova-Nikolsko-Rzhevskyy-Papell (2007): dollar/mark exchange rate predictable only with real-time data

  28. Forecasting • Summary: for forecasting, sometimes data vintage matters, other times it doesn’t

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

  30. Monetary Policy

  31. 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?

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

  33. Monetary Policy: Data Revisions • How Much Does It Matter for Monetary Policy that Data Are Revised? • Croushore (2008): PCE revisions could mislead the Fed • Maravall-Pierce (1986): The Fed optimally signal extracts from the noise in money data, so data revisions would not significantly affect monetary policy • Kugler et al. (2005): Monetary policy shojuld be less aggressive because of data revisions

  34. Monetary Policy: Data Revisions • How Misleading Is Monetary Policy Analysis Based on Final Data Instead of Real-Time Data? • Croushore-Evans (2006): Data revisions do not significantly affect measures of monetary policy shocks in recursive systems, but they make identification of simultaneous systems problematic

  35. 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

  36. Monetary Policy: Analytical Revisions • What Happens When Economists or Policymakers Revise Conceptual Variables? • Output gap • Natural rate of unemployment • Equilibrium real interest rate • Concepts are never observed, but are centerpiece of macroeconomic theory

  37. Monetary Policy: Analytical Revisions • Orphanides (2001): Fed overreacted to perceived output gap in 1970s, causing Great Inflation; but output gap was mismeasured

  38. Output Gap Revisions • Most U.S. analysts look at CBO measure, but it is revised extensively over time • Problem is especially acute at the end of the sample

  39. 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 • Most work is statistical; perhaps a theoretical breakthrough is needed

  40. Macroeconomic Research

  41. Macroeconomic Research • How Is Macroeconomic Research Affected By Data Revisions? • Croushore-Stark (2003): how results from key macro studies are affected by alternative vintages • Boschen-Grossman (1982): testing neutrality of money under rational expectations: support for RE with revised data, but not with real-time data

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