Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Further Evidence on the Usefulness of Real-Time Datasets for Economic Forecasting

1 Research Department, Inter-American Development Bank, 1300 New York Ave, NW, Washington, DC 20577, USA
2 Department of Economics, Rutgers University, New Brunswick, NJ 08901, USA

In this paper, we assess the relevance of real-time datasets for forecasting. We construct a variety of real-time prediction models and evaluate their performance in a series of ex-ante prediction experiments that are designed to mimic forecasting approaches used when constructing forecasts in real-time for output, prices and money. We assess the models within univariate and multivariate frameworks by including revision errors as regressors, allowing us to examine the marginal predictive content of the revision process. In another multivariate application for output we add money, thus examining the real-time predictive content of money for income. The most important result we obtain is that the choice of which release of data to predict seems not to have an impact on which releases of data should be used in estimation and prediction construction but that differences in how to utilize realtime datasets do arise when the variable being modelled and predicted changes. Overall our findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. This underscores the importance of collecting and maintaining such real-time datasets.
  Figure/Table
  Supplementary
  Article Metrics

References

1. Amato J, Swanson NR (2001) The Real-Time Predictive Content of Money for Output. J Monet Econ 48: 324.

2. Aruoba SB (2008) Real-Time Measurement of Business Conditions. J Bus Econ Stat, forthcom.

3. Aruoba SB, Diebold FX, Scotti C (2008) Data Revisions are not Well-Behaved. J Money, Credit and Banking 40: 319-340.    

4. Ashley R, Granger, Schmalensee R (1980) Advertising and Aggregate Consumption: An Analysis of Causality, Econometrica. 48: 1149-1167.

5. Bernanke BS, Boivin J (2003) Monetary Policy in a Data-Rich Environment, J Monet Econ 50: 525546.

6. Chao J, Corradi V, Swanson NR (2001) An out-of-sample Test for Granger Causality, Macroeconomic Dynamics 5: 598-620.

7. Clark TE, McCrackenMW(2001) Tests of Equal Forecast Accuracy and Encompassing for Nested Models. J Econ 105: 85-110.

8. Clark TE, McCracken MW (2005) Evaluating Direct Multistep Forecasts. Econ Reviews 24: 369-404.    

9. Clark TE, McCracken MW (2009) Tests of Equal Predictive Ability with Real-Time Data. J Business & Economic Stat 27: 441-454.

10. Corradi V, Swanson NR (2002) A Consistent Test for Out of Sample Nonlinear Predictive Ability. J Econ 110:353-381.    

11. Corradi VA, Fernandez, Swanson NR (2009) Information in the Revision Process of Real-Time Datasets. J Business & Economic Stat 27: 455-467.

12. Corradi V, Swanson NR (2006) Predictive Density Evaluation, in: Handbook of Economic Forecasting, eds. Clive W.J. Granger, Graham Elliot and Allan Timmermann, Elsevier, Amsterdam, 197-284.

13. Croushore D (2006) Forecasting with Real-Time Macroeconomic Data, in: Handbook of Economic Forecasting, eds. CliveW.J. Granger, Graham Elliot and Allan Timmermann, Elsevier, Amsterdam, 961982.

14. Croushore D, Stark T (2001) A Real-Time Dataset for Macroeconomists. J Econ 105: 111130.

15. Croushore D, Stark T (2003) A Real-Time Dataset for Macroeconomists: Does Data Vintage Matter? Rev Econ and Stat 85: 605617.

16. Diebold FX, Mariano RS (1995) Comparing Predictive Accuracy. J Business and Economic Stat 13: 253-263.

17. Diebold FX, Rudebusch GD (1991) Forecasting Output with the Composite Leading Index: A Real-Time Analysis. J American Stat Assoc 86: 603610.

18. Faust J, Wright J (2009) Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset. J Business & Economic Stat.

19. Franses PH (2013) Data Revisions and Periodic Properties of Macroeconomic Data. Econ Letters 120: 139-141.    

20. Franses PH, Segers R (2010) Seasonality in Revisions in Macroeconomic Data. J O cial Stat 26: 361-369.

21. Gallo GM, Marcellino M (1999) Ex Post and Ex Ante Analysis of Provisional Data. J Forec 18: 421433.

22. Garratt A, Koop G, Mise E, et al. (2009) Real-Time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty. J Business and Economic Stat 27: 480-491.    

23. Ghysels E, Swanson NR, Callan M (2002), Monetary Policy Rules with Model and Data Uncertainty. Southern Econ J 69, 239-265.

24. Gilbert T (2011), Information Aggregation Around Macroeconomic Announcements: Revisions Matter. J Financial Econ 101: 114-131.

25. Hamilton JD, Perez-Quiros G (1996) What Do the Leading Indicators Lead? J Business 69: 2749.

26. Kavajecz KA, Collins S (1995) Rationality of Preliminary Money Stock Estimates. Rev Econ and Stat 77: 3241.

27. Keane MP, Runkle DE (1990) Testing the Rationality of Price Forecasts: New Evidence from Panel Data. American Econ Rev 80: 714735.

28. Mankiw NG, Runkle DE, Shapiro MD(1984) Are Preliminary Announcements of the Money Stock Rational Forecasts? J Monetary Econ 14: 1527.

29. Mankiw NG, Shapiro MD (1986) News or Noise: an Analysis of GNP Revisions. Surv of Current Bus 66: 2025.

30. Mariano RS, Tanizaki H (1995) Prediction of Final Data with Use of Preliminary and/or Revised Data. J Forec 14: 351380.

31. Mork KA (1987) Aint Behavin: Forecast Errors and Measurement Errors in Early GNP Estimates. J Business & Economic Stat 5: 165175.

32. Rathjens P, Robins RP (1995) Do Government Agencies Use Public Data: the Case of GNP. Rev Econ and Stat 77: 170-172.    

33. Robertson JC, Tallman EW (1998) Data Vintages and Measuring Forecast Performance, Federal Reserve Bank of Atlanta Economic Review 83 (Fourth Quarter), 420.

34. Stock JH, Watson MW (1989) Interpreting the Evidence on Money-Income Causality. J Econ 40: 161-181.    

35. Swanson NR, Ghysels E, Callan M (1999) A Multivariate Time Series Analysis of the Data Revision Process for Industrial Production and the Composite Leading Indicator, in R.F. Engle and H. White, eds., Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W.J. Granger, Oxford: Oxford University Press, 45-75.

36. Swanson NR, Dijk D(2006) Are Statistical Reporting Agencies Getting It Right? Data Rationality and Business Cycle Asymmetry. J Business & Economic Stat 24: 2442.

Copyright Info: © 2017, Norman R. Swanson, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved