Component versus tradicional models to forecast quarterly national account aggregates: a Monte Carlo experiment

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Instituto Complutense de Análisis Económico. Universidad Complutense de Madrid
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Econometric models applied to observed data, specified and estimated using traditional Box-Jenkins techniques, have been widely used to forecast Quarterly National Account (QNA) aggregates. We assess the extent to which an alternative forecasting procedure, based on component models, improves the forecasting accuracy of traditional methods. Component models distinguish between the stochastic processes underlying the low- and the high-frequency component of time series, while traditional methods do not. Relationships between QNA aggregates and their coincident indicators are often significantly different for diverse frequencies, as suggested by even an informal examination of empirical evidence. Under these circumstances, a Monte Carlo out-of-sample experiment reveals that component models improve the forecasting accuracy of traditional methods to predict QNA aggregates when their coincident indicators play an important role in such predictions. Otherwise, specially when dealing with pure univariate specifications, traditional procedures likely beat component methods. We illustrate these findings with several applications for the Spanish economy.
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Baffigia, A. R. Golinelli and G. Parigia (2004). Bridge models to forecast the euro area GDP, International Journal of Forecasting, 20, 447— 460. Bates, J. M.and C. W. Granger (1969). The combination of forecasts, Operational Research Quarterly, 20, 451—468. Baxter, M. and R.G. King (1999). Measuring Business Cycles: Approximate BandPass Filters for Economic Time Series, Review of Economics and Statistics, Vol. 81(4), 575-93. Beveridge, S. and C.R. Nelson (1981). A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the “Business Cycle”, Journal of Monetary Economics, Vol. 7, 151-74. Boschan, C. and W.W. Ebanks (1978). The Phase-Average Trend: A New Way of Measuring Growth, in 1978 Proceedings of the Business and Economic Statistics Section, American Statistical Association, Washington, D.C. Bujosa, M., García-Ferrer, A. and P. Young (2001). An ARMA Representation of Unobserved Component Models under Generalized Random Walk Specifications: New Algorithms and Examples, UAM: WP01-6, oct. 2001. Diebold, F.X. and R.S. Mariano (1995). Comparing Predictive Accuracy, Journal of Business and Economic Statistics 13, 253-263. Friedman, M. and A.J. Schwartz (1963). Money and Business Cycles, Review of Economics and Statistics, 45 (suppl.), 32-64. García-Ferrer, A., del Hoyo, J., Novales, A. and C. Sebastian (1994). The Use of Economic Indicators to Forecast the Spanish Economy: Preliminary Results from the ERISTE Project. Paper presented at the XIV Intl. Symp. of Forecasting, Stockholm, June 1994. García-Ferrer, A., del Hoyo, J., Novales, A. and P.C. Young (1996). Recursive Identifi-cation, Estimation and Forecasting of Nonstationary Time Series with Applications to GNP International Data. In: Berry, D. A. el al. (Eds.), Bayesian Analysis in Statistics and Econometrics: Essays in Honour of Arnold Zellner, John Wiley, New York, pp. 15-27. García-Ferrer, A. and R. Queralt (1998). Using Long-, Medium-, and Short-Term Trends to Forecast Turning Points in the Business Cycle: Some International Evidence. Studies in Nonlinear Dynamics and Econometrics 3(2), 79-105. García-Ferrer, A., Queralt, R. and C. Blázquez (2001). A Growth Cycle Characterization and Forecasting of the Spanish Economy: 1970-1998. International Journal of Forecasting 17, 517-532. García-Ferrer, A. and P. Poncela (2002). Forecasting international GNP growth rates through common factors and other procedures. Journal of Forecasting, 21, 225-244. Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge, New York and Melbourne. Harvey, A.C. (2000). Trend Analysis, University of Cambridge, Faculty of Economics and Politics, Manuscript. Hibon, M. and T. Evgeniou (2004). To combine or not to combine: selecting among forecasts and their combinations, International Journal of Forecasting, forthcoming. Hodrick, R.J. and E.C. Prescott (1997). Postwar U.S. Business Cycles: an Empirical Investigation, Journal of Money Credit and Banking, Vol. 29(1), 1-16. Klein, P.A. and G.H. Moore (1985). Monitoring Growth Cycles in Market-Oriented Countries: Developing and Using International Economic Indicators, Ballinger for NBER, Cambridge, Mass. Loungani, P. (2001). How Accurate are Private Sector Forecasts? Cross-country Evidence from Consensus Forecasts of Output Growth,International Journal of Forecasting 17 (2001) 419—432. Meltzer, A. H. (1995). Monetary, Credit and (other) Transmission Processes, Journal of Economic Perspective, 9, 49-73. Meltzer, A. H. (1999). The Transmission Process, Mimeo, Carnegie Mellon University and the American Enterprise Institute. Mintz, I. (1969). Dating Postwar Business Cycles: Methods and their Application to Western Germany, 1950-1967, Occasional Paper NBER No. 107, New York. Mintz, I. (1972). Dating American Growth Cycle, in Zarnowitz, V., ed., The Business Cycle Today, NBER, New York. Moore, G.H. and V. Zarnowitz (1986). The Development and Role of the National Bureau of Economic Research’s Business Cycle Chronologies, in Gordon, R.A., ed., The American Business Cycle: Continuity and Change, University of Chicago Press for NBER, Chicago. Rotemberg, J.J. (1999). A Heuristic Method for Extracting Smooth Trends from Economic Time Series, NBER Working Paper No. 7439. Watson, M. (1986). Univariate Detrending Methods with Stochastic Trends, Journal of Monetary Economics, Vol. 18(1), 49-75. Young, P.C. (1984). Recursive Estimation in Time Series Analysis, Springer Verlag, Berlin. Young, P.C. (1994). Time Variable Parameter and Trend Estimation in Non-Stationary Economic Time Series. Journal of Forecasting 13(2), 179-210. Zarnowitz, V. and A. Ozyildirim (2002). Time Series Decomposition and Measurement of Business Cycles, Trend and Growth Cycles, NBER Working Paper 8736.