Clements, Michael P. and Galvão, Ana Beatriz C. (Ana Beatriz Camatari) (2006) Macroeconomic forecasting with mixed frequency data: forecasting US output growth and inflation. Working Paper. University of Warwick, Department of Economics, Coventry.

Preview

PDF WRAP_Clements_twerp_773.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader Download (607Kb)

Abstract

Although many macroeconomic series such as US real output growth are sampled quarterly, many potentially useful predictors are observed at a higher frequency. We look at whether a recently developed mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth and inflation. We carry out a number of related real-time forecast comparisons using various indicators as explanatory variables. We find that MIDAS model forecasts of output growth are more accurate at horizons less than one quarter using coincident indicators ; that MIDAS models are an effective way of combining information from multiple indicators ; and that the forecast accuracy of the unemployment-rate Phillips curve for inflation is enhanced using the MIDAS approach.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	H Social Sciences > HG Finance
Divisions:	Faculty of Social Sciences > Economics
Library of Congress Subject Headings (LCSH):	Economic forecasting, Econometric models, Sampling (Statistics), Phillips curve, Unemployment -- Effect of inflation on -- Mathematical models, Inflation (Finance) -- Mathematical models
Series Name:	Warwick economic research papers
Publisher:	University of Warwick, Department of Economics
Place of Publication:	Coventry
Date:	July 2006
Number:	No.773
Number of Pages:	36
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
References:	Bhansali, R. J. (2002). Multi-step forecasting. In Clements, M. P., and Hendry, D. F. (eds.), A Companion to Economic Forecasting, pp. 206—221: Oxford: Blackwells. Birchenhall, C. R., Jessen, H., Osborn, D., and Simpson, P. (1999). Predicting U.S. business cycle regimes. Journal of Business & Economic statistics, 17, No. 3, (July), 313—323. Breiman, L. (1996). Bagging predictors. Machine Learning, 36, 105—139. Campos, J., Hendry, D. F., and Krolzig, H.-M. (2003). Consistent model selection by an automatic Gets approach. Oxford Bulletin of Economics and Statistics, 65, 803—819. Chong, Y. Y., and Hendry, D. F. (1986). Econometric evaluation of linear macro-economic models. Review of Economic Studies, 53, 671—690. Reprinted in Granger, C. W. J. (ed.) (1990), Modelling Economic Series. Oxford: Clarendon Press. Chow, G., and Lin, A. (1971). Best linear unbiased interpolation, distribution and extrapolation of time series by related time series. Review of Economics and Statistics, 53, 372—375. Clark, T. E., and McCracken, M. W. (2001). Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics, 105, 85—110. Clark, T. E., and McCracken, M. W. (2005). Evaluating direct multi-step forecasts. Econometric Reviews, 24, 369—404. Clements, M. P., and Galvão, A. B. (2006). Combining predictors and combining information in modelling: Forecasting US recession probabilities and output growth. In Milas, C., Rothman, P., and van Dijk, D. (eds.), Nonlinear Time Series Analysis of Business Cycles. Contributions to Economic Analysis Series, pp. 55—73: Elsevier. Clements, M. P., and Hendry, D. F. (1993). On the limitations of comparing mean squared forecast errors. Journal of Forecasting, 12, 617—637. With discussion. Reprinted in Mills, T. C. (ed.) (1999), Economic Forecasting. The International Library of Critical Writings in Economics. Cheltenham: Edward Elgar. 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 Econometrics, 105(November), 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(3), 605—617. Dhrymes, P. J. (1971). Distributed Lags: Problems of Estimation and Formulation. San Francisco: Holden Day. Diebold, F. X., and Lopez, J. A. (1996). Forecast evaluation and combination. In Maddala, G. S., and Rao, C. R. (eds.), Handbook of Statistics, Vol. 14, pp. 241—268: Amsterdam: North—Holland. Diebold, F. X., and Rudebusch, G. D. (1991). Forecasting output with the composite leading index: A real-time analysis. Journal of the American Statistical Association, 86, 603—610. 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. Faust, J., Rogers, J. H., and Wright, J. H. (2005). News and noise in G-7 GDP announcements. Journal of Money, Credit and Banking, 37 (3), 403—417. Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2000). The generalized factor model: Identification and estimation. Review of Economics and Statistics, 82, 540—554. Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004a). The MIDAS touch: Mixed Data Sampling regression models. mimeo, Chapel Hill, N.C., Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004b). Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics. Forthcoming. Ghysels, E., Sinko, A., and Valkanov, R. (2004c). MIDAS regressions: Further results and new directions. Econometric Reviews. Forthcoming. Ghysels, E., and Wright, J. (2005). Forecasting Professional Forecasters. mimeo, Chapel Hill, N.C. Hendry, D. F., and Krolzig, H.-M. (2005). The properties of automatic Gets modelling. Economic Journal, 115, C32—C61. Hendry, D. F., and Mizon, G. E. (1978). Serial correlation as a convenient simplification, not a nuisance: A comment on a study of the demand for money by the Bank of England. Economic Journal, 88, 549—563. Reprinted in Hendry, D. F., Econometrics: Alchemy or Science? Oxford: Blackwell Publishers, 1993, and Oxford University Press, 2000. Inoue, A., and Kilian, L. (2005). How useful is bagging in forecasting economic time series? a case study of U.S. CPI inflation. mimeo, North Carolina State University. Joutz, F., and Stekler, H. O. (2000). An evaluation of the predictions of the Federal Reserve. International Journal of Forecasting, 16, 17—38. Karamouzis, N., and Lombra, N. (1989). Federal Reserve policymaking: An overview and analysis of the policy process. Carnegie Rochester Conference Series on Public Policy, 30, 7—62. Kilian, L. (1999). Exchange rates and monetary fundamentals: What do we learn from long-horizon regressions. Journal of Applied Econometrics, 14, 491—510. 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. McCracken, M. W. (2004). Asymptotics for out-of-sample tests of causality. Discussion paper. Manuscript, University of Missouri. Miller, P. J., and Chin, D. M. (1996). Using monthly data to improve quarterly model forecasts. Federal Reserve Bank of Minneapolis Quarterly Review, 20, 16—33. (2). Montgomery, A. L., Zarnowitz, V., Tsay, R. S., and Tiao, G. C. (1998). Forecasting the U.S. unemployment rate. Journal of the American Statistical Association, 93, 478—493. Newbold, P., and Harvey, D. I. (2002). Forecasting combination and encompassing. In Clements, M. P., and Hendry, D. F. (eds.), A Companion to Economic Forecasting, pp. 268—283: Oxford: Blackwells. 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 performance. Federal Reserve Bank of Atlanta Economic Review, Fourth Quarter, 4—20. Sims, C. A. (1974). Distributed lags. In Intriligator, M. D., and Kendrick, D. A. (eds.), Frontiers of Quantitative Economics, Vol. 2, Ch. 5. Amsterdam: North-Holland Publishing Company. Stock, J. H., and Watson, M. W. (1999). Forecasting inflation. Journal of Monetary Economics, 44, 293—335. Stock, J. H., and Watson, M. W. (2003). How Did Leading Indicator Forecasts Perform During the 2001 Recession. Federal Reserve Bank of Richmond, Economic Quaterly, 89/3, Summer, 71—90. Timmermann, A. (2006). Forecast combinations. In Elliott, G., Granger, C., and Timmermann, A. (eds.), Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, pp. 135—196: Elsevier, Horth-Holland. West, K. D. (1996). Asymptotic inference about predictive ability. Econometrica, 64, 1067—1084. West, K. D. (2006). Forecasting evaluation. In Elliott, G., Granger, C., and Timmermann, A. (eds.), Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, pp. 99—134: Elsevier, Horth-Holland.
URI:	http://wrap.warwick.ac.uk/id/eprint/1426

Request changes to a record

Actions (login required)

View Item