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Real-time forecasting of inflation and output growth in the presence of data revisions

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Clements, Michael P. and Galvão, Ana Beatriz (2010) Real-time forecasting of inflation and output growth in the presence of data revisions. Working Paper. Coventry: University of Warwick. Dept. of Economics. (Warwick economics research paper series (TWERPS), Vol.2010).

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Abstract

We show how to improve the accuracy of real-time forecasts from models that include autoregressive terms by estimating the models on 'lightly-revised' data instead of using data from the latest-available vintage. Forecast accuracy is improved by reorganizing the data vintages employed in the estimation of the model in such a way that the vintages used in estimation are of a similar maturity to the data in the forecast loss function. The size of the expected reductions in mean squared error depend on the characteristics of the data revision process. Empirically, we find RMSFE gains of 2-4% when forecasting output growth and inflation with AR models, and gains of the order of 8% with ADL models.

Item Type: Working or Discussion Paper (Working Paper)
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics
Library of Congress Subject Headings (LCSH): Economic forecasting, Real-time data processing, Econometric models
Series Name: Warwick economics research paper series (TWERPS)
Publisher: University of Warwick. Dept. of Economics
Place of Publication: Coventry
Date: 2010
Volume: Vol.2010
Number: No.953
Status: Not Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
References: Aruoba, S. B. (2008). Data revisions are not well-behaved. Journal of Money, Credit and Banking, 40, 319-340. Clements, M. P., and Galvão, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics, 26, 546-554. No. 4. Clements, M. P., and Galvão, A. B. (2009). Forecasting US output growth using leading indicators: An appraisal using MIDAS models. Journal of Applied Econometrics, 24, 1187-1206. Clements, M. P., and Hendry, D. F. (1999). Forecasting Non-stationary Economic Time Series. Cambridge, Mass.: MIT Press. Corradi, V., Fernandez, A., and Swanson, N. R. (2009). Information in the revision process of real-time datasets. Journal of Business and Economic Statistics, 27, 455-467. Croushore, D. (2006). Forecasting with real-time macroeconomic data. In Elliott, G., Granger, C., and Timmermann, A. (eds.), Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, pp. 961-982: Elsevier, Horth-Holland. Croushore, D., and Stark, T. (2001). A real-time data set for macroeconomists. Journal of Econo- metrics, 105, 111-130. Croushore, D., and Stark, T. (2003). A real-time data set for macroeconomists: Does the data vintage matter?. The Review of Economics and Statistics, 85, 605-617. Cunningham, A., Eklund, J., Je¤ery, C., Kapetanios, G., and Labhard, V. (2009). A state space approach to extracting the signal from uncertain data. Journal of Business & Economic Statistics. Diebold, F. X., and Rudebusch, G. D. (1991a). Forecasting output with the composite leading index: A real-time analysis. Journal of the American Statistical Association, 86, 603-610. Diebold, F. X., and Rudebusch, G. D. (1991b). Turning point prediction with the composite leading index: An ex ante analysis. In Lahiri, K., and Moore, G. H. (eds.), Leading economic indicators: New approaches and forecasting records, pp. 231-256: Cambridge University Press, Cambridge. Faust, J., Rogers, J. H., and Wright, J. H. (2003). Exchange rate forecasting: The errors we've really made. Journal of International Economic Review, 60, 35-39. Fildes, R. A., and Makridakis, S. (1995). The impact of empirical accuracy studies on time series analysis and forecasting. International Statistical Review, 63, 289-308. Fixler, D. J., and Grimm, B. T. (2005). Reliability of the NIPA estimates of U.S. economic activity. Survey of Current Business, 85, 9-19. Fixler, D. J., and Grimm, B. T. (2008). The reliability of the GDP and GDI estimates. Survey of Current Business, 88, 16-32. Garratt, A., Lee, K., Mise, E., and Shields, K. (2008). Real time representations of the output gap. Review of Economics and Statistics, 90, 792-804. Garratt, A., Lee, K., Mise, E., and Shields, K. (2009). Real time representations of the UK output gap in the presence of model uncertainty. International Journal of Forecasting, 25, 81-102. Harrison, R., Kapetanios, G., and Yates, T. (2005). Forecasting with measurement errors in dy- namic models. International Journal of Forecasting, 21, 595-607. Harvey, A. C., McKenzie, C. R., Blake, D. P. C., and Desai, M. J. (1983). Irregular data revi- sions. In Zellner, A. (ed.), Applied Time Series Analysis of Economic Data, pp. 329-347: US Department of Commerce, Washington D.C., Economic Research Report ER-5. Hecq, A., and Jacobs, J. P. A. M. (2009). On the VAR-VECM representation of real time data. Discussion paper, mimeo, University of Maastricht, Department of Quantitative Economics. Howrey, E. P. (1978). The use of preliminary data in economic forecasting. The Review of Economics and Statistics, 60, 193-201. Howrey, E. P. (1984). Data revisions, reconstruction and prediction: an application to inventory investment. The Review of Economics and Statistics, 66, 386-393. Jacobs, J. P. A. M., and van Norden, S. (2007). Modeling data revisions: Measurement error and dynamics of 'true' values. Technical report cref 07-09, HEC, Montreal. Kishor, N. K., and Koenig, E. F. (2010). VAR estimation and forecasting when data are subject to revision. Journal of Business and Economic Statistics. Forthcoming. Koenig, E. F., Dolmas, S., and Piger, J. (2003). The use and abuse of real-time data in economic forecasting. The Review of Economics and Statistics, 85(3), 618-628. Landefeld, J. S., Seskin, E. P., and Fraumeni, B. M. (2008). Taking the pulse of the economy. Journal of Economic Perspectives, 22, 193-216. Makridakis, S., and Hibon, M. (2000). The M3 Competition: results, conclusions and implications. International Journal of Forecasting, 16, 451-476. Mankiw, N. G., and Shapiro, M. D. (1986). News or noise: An analysis of GNP revisions. Survey of Current Business (May 1986), US Department of Commerce, Bureau of Economic Analysis, 20-25. Orphanides, A. (2001). Monetary policy rules based on real-time data. American Economic Review, 91(4), 964-985. Orphanides, A., and van Norden, S. (2005). The reliability of inflation forecasts based on output gaps in real time. Journal of Money, Credit and Banking, 37, 583-601. Patterson, K. D. (1995). An integrated model of the data measurement and data generation processes with an application to consumers' expenditure. Economic Journal, 105, 54-76. Patterson, K. D. (2003). Exploiting information in vintages of time-series data. International Journal of Forecasting, 19, 177-197. Robertson, J. C., and Tallman, E. W. (1998). Data vintages and measuring forecast model perfor- mance. Federal Reserve Bank of Atlanta Economic Review, Fourth Quarter, 4-20. Sargent, T. J. (1989). Two models of measurements and the investment accelerator. Journal of Political Economy, 97, 251-287. Siklos, P. L. (2008). What can we learn from comprehensive data revisions for forecasting inflation: Some US evidence. In Rapach, D. E., and Wohar, M. E. (eds.), Forecasting in the Presence of Structural Breaks and Model Uncertainty. Frontiers of Economics and Globalization. Volume 3, pp. 271-299: Emerald. Stark, T., and Croushore, D. (2002). Forecasting with a real-time data set for macroeconomists. Stock, J. H., and Watson, M. W. (2003). How Did Leading Indicator Forecasts Perform During the 2001 Recession. Federal Reserve Bank of Richmond, Economic Quarterly, 89/3, 71-90. Stock, J. H., and Watson, M. W. (2008). Phillips Curve Inflation Forecasts. Working paper 14322, NBER, Cambridge, MA.
URI: http://wrap.warwick.ac.uk/id/eprint/41095

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