Boivin, Jean and Ng, Serena (2005): Understanding and Comparing Factor-Based Forecasts. Published in: International Journal of Central Banking , Vol. Volume, No. Number 3 (1. December 2005): pp. 117-151.
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Forecasting using "diffusion indices" has received a good deal of attention in recent years. The idea is to use the common factors estimated from a large panel of data to help forecast the series of interest. This paper assesses the extent to which the forecasts are influenced by (i) how the factors are estimated and/or (ii) how the forecasts are formulated. We find that for simple data-generating processes and when the dynamic structure of the data is known, no one method stands out to be systematically good or bad. All five methods considered have rather similar properties, though some methods are better in long-horizon forecasts, especially when the number of time series observations is small. However, when the dynamic structure is unknown and for more complex dynamics and error structures such as the ones encountered in practice, one method stands out to have smaller forecast errors. This method forecasts the series of interest directly, rather than the common and idiosyncratic components separately, and it leaves the dynamics of the factors unspecified. By imposing fewer constraints, and having to estimate a smaller number of auxiliary parameters, the method appears to be less vulnerable to misspecification, leading to improved forecasts.
|Item Type:||MPRA Paper|
|Original Title:||Understanding and Comparing Factor-Based Forecasts|
|Subjects:||G - Financial Economics > G0 - General > G00 - General
G - Financial Economics > G0 - General
|Depositing User:||Terry Woodard|
|Date Deposited:||15. Nov 2006|
|Last Modified:||17. Feb 2013 17:14|
Anderson, Theodore W., and H. Rubin. 1956. “Statistical Inference in Factor Analysis.” In Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. V, ed. J. Neyman, 114–50. Berkeley: University of California Press.
Bai, Jushan. 2003. “Inferential Theory for Factor Models of Large Dimensions.” Econometrica 71 (1): 135–72.
Bai, Jushan, and Serena Ng. 2002. “Determining the Number of Factors in Approximate Factor Models.” Econometrica 70 (1): 191–221.
Boivin, Jean, and Serena Ng. 2004. “Are More Data Always Better for Factor Analysis?” Forthcoming in Journal of Econometrics.
Brillinger, David R. 1981. Time Series: Data Analysis and Theory. San Francisco: Wiley.
Chamberlain, Gary, and Michael Rothschild. 1983. “Arbitrage, Factor Structure and Mean-Variance Analysis on Large Asset Markets.” Econometrica 51:1305–24.
Connor, Gregory, and Robert A. Korajzcyk. 1986. “Performance Measurement with the Arbitrage Pricing Theory: A New Framework for Analysis.” Journal of Financial Economics 15 (3): 373–94.
Forni, Mario, Marc Hallin, Marco Lippi, and Lucrezia Reichlin. 2000. “The Generalized Dynamic Factor Model: Identification and Estimation.” Review of Economics and Statistics 82 (4): 540–54.
2001. “Coincident and Leading Indicators for the Euro Area.” Economic Journal 111 (471): C82–5.
2004. “The Generalized Factor Model: Consistency and Rates.” Journal of Econometrics 119 (2): 231–55.
2005. “The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting.” Journal of the American Statistical Association 100 (471): 830–40.
Forni, Mario, and Lucrezia Reichlin. 2001. “Federal Policies and Local Economies: Europe and the US.” European Economic Review 45 (1): 109–34.
Grenouilleau, Daniel. 2004. “A Sorted Leading Indicators Dynamic (SLID) Factor Model for Short-Run Euro-Area DGP Forecasting.” European Commission Economic Papers 219.
Inoue, Atsushi, and Lutz Kilian. 2003. “Bagging Time Series Models.” Unpublished Manuscript, North Carolina University.
Kapetanios, George, and Massimiliano Marcellino. 2002. “A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions.” Draft, Bocconi University.
Kitchen, John, and Ralph M. Monaco. 2003. “Real-Time Forecasting in Practice: The U.S. Treasury Staff’s Real-Time GDP Forecast System.” Business Economics (October): 10–19.
Marcellino, Massimiliano, James Stock, and Mark Watson. 2004. “A Comparison of Direct and Iterated AR Methods for Forecasting Macroeconomic Time Series h-steps Ahead.” Mimeo, Princeton University.
Stock, James H., and Mark W. Watson. 1999. “Forecasting Inflation.” Journal of Monetary Economics 44 (2): 293–335.
2002a. “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association 97 (460): 1167–79.
2002b. “Macroeconomic Forecasting Using Diffusion Indexes.” Journal of Business and Economic Statistics 20 (2): 147–62.
2004a. “An Empirical Comparison of Methods for Forecasting Using Many Predictors.” Mimeo, Princeton University.
2004b. “Forecasting with Many Predictors.” In The Handbook of Economic Forecasting, ed. G. Elliott. Elsevier Science.
Stock, James H., Mark W. Watson, and Massimiliano Marcellino.
2003. “Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information.” European Economic Review 47 (1): 1–18.