Pooling in dynamic panel data models: an application to forecasting GDP growth rates (2000)

doi:10.1017/CBO9780511493171.019

18 - Pooling in dynamic panel data models: an application to forecasting GDP growth rates (2000)

Published online by Cambridge University Press: 24 October 2009

André J. Hoogstrate ,

Franz C. Palm and

Gerard A. Pfann

Edited by

Arnold Zellner and

Franz C. Palm

Show author details

André J. Hoogstrate: Affiliation:
Ministry of Justice, Netherlands Forensic Institute, Rijswijk
Franz C. Palm: Affiliation:
Professor of Econometrics, Faculty of Economics and Business Administration, Universiteit Maastricht
Arnold Zellner: Affiliation:
University of Chicago
Franz C. Palm: Affiliation:
Universiteit Maastricht, Netherlands

Book contents

Get access

Summary

In this chapter, we analyze issues of pooling models for a given set of N individual units observed over T periods of time. When the parameters of the models are different but exhibit some similarity, pooling may lead to a reduction of the mean squared error of the estimates and forecasts. We investigate theoretically and through simulations the conditions that lead to improved performance of forecasts based on pooled estimates. We show that the superiority of pooled forecasts in small samples can deteriorate as the sample size grows. Empirical results for postwar international real gross domestic product growth rates of 18 Organization for Economic Cooperation and Development countries using a model put forward by Garcia-Ferrer, Highfield, Palm, and Zellner and Hong, among others illustrate these findings. When allowing for contemporaneous residual correlation across countries, pooling restrictions and criteria have to be rejected when formally tested, but generalized least squares (GLS)-based pooled forecasts are found to outperform GLS-based individual and ordinary least squares-based pooled and individual forecasts.

Panel data are used more and more frequently in business and economic studies. Sometimes a given number of entities is observed over a longer period of time, whereas traditionally panel data are available for a large and variable number of entities observed for a fixed number of time periods (e.g. see Baltagi 1995 for a[n] … overview; Maddala 1991; Maddala, Trost, and Li 1994).

Type: Chapter
Information: The Structural Econometric Time Series Analysis Approach , pp. 590 - 611

DOI: https://doi.org/10.1017/CBO9780511493171.019 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Baltagi, B. H. (1995), Econometric Analysis of Panel Data (New York, John Wiley)

Bernard, A. B. and Durlauf, S. N. (1995), “Convergence in international output,” Journal of Applied Econometrics 10, 97–108CrossRef Google Scholar

Bilson, J. F. O. (1981), “The ‘Speculative Efficiency’ Hypothesis,” Journal of Business 54, 435–51CrossRef Google Scholar

Blattberg, R. C. and George, E. I. (1991), “Shrinkage estimation of promotional elasticities: seemingly unrelated equations,” Journal of the American Statistical Association 86, 304–15CrossRef Google Scholar

Breusch, T. S. and Pagan, A. R. (1980), “The Lagrange multiplier test and its applications to model specifications in econometrics,” Review of Economic Studies 47, 239–53CrossRef Google Scholar

Chib, S. and Greenberg, E. (1995), “Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models,” Journal of Econometrics 68, 339–60CrossRef Google Scholar

Garcia-Ferrer, A., Highfield, R. A., Palm, F. C., and Zellner, A. (1987), “Macroeconomic forecasting using pooled international data,” Journal of Business and Economic Statistics 5(1), 53–67; chapter 13 in this volumeGoogle Scholar

Goodnight, J. and Wallace, T. D. (1972), “Operational techniques and tables for making weak MSE tests for restrictions in regressions,” Econometrica 40, 699–709CrossRef Google Scholar

Haitovsky, Y. (1990), “Seemingly unrelated regression with linear hierarchical structure,” in S. Geisser, J. S. Hodges, J. S. Press, and A. Zellner (eds.), Bayesian and Likelihood Methods in Statistics and Econometrics (Amsterdam, North-Holland, 163–77

Lindley, D. V. and Smith, A. F. M. (1972), “Bayesian estimates for the linear model,” Journal of the Royal Statistical Society Series B 34, 1–41Google Scholar

Maddala, G. S. (1991), “To pool or not to pool: that is the question,” Journal of Quantitative Economics 7, 255–64Google Scholar

Maddala, G. S., R. P. Trost and H. Li (1994), “Estimation of short run and long run elasticities of energy demand from panel data using shrinkage estimators,” Paper presented at the Econometric Society European Meeting, Maastricht, August 29–September 2

McElroy, M. B. (1977), “Weaker MSE criteria and tests for linear restrictions in regression models with nonspherical disturbances,” Journal of Econometrics 6, 389–94CrossRef Google Scholar

Min, C. and Zellner, A. (1993), “Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates,” Journal of Econometrics, Annals 56, 89–118; chapter 17 in this volumeCrossRef Google Scholar

Mittnik, S. (1990), “Macroeconomic forecasting using pooled international data,” Journal of Business and Economic Statistics 8, 205–8Google Scholar

Nandram, B. and Petruccelli, J. D. (1997), “A Bayesian analysis of autoregressive time series panel data,” Journal of Business and Economic Statistics 15, 328–34Google Scholar

Palm, F. C. and Sneek, J. M. (1984), “Significance tests and spurious correlation in regression models with autocorrelated errors,” Statistical Papers 25, 87–105Google Scholar

Stock, J. H. and M. W. Watson (1989), “New indexes of coincident and leading economic indicators,” in O. J. Blanchard and S. Fischer (eds.), NBER Macroeconomics Annual 1989 (Cambridge, Mass., MIT Press), 351–94CrossRef

Wallace, T. D. (1972), “Weaker criteria and tests for linear restrictions in regressions,” Econometrica 40, 689–709CrossRef Google Scholar

Wallace, T. D. and Toro-Vizcarrondo, C. E. (1969), “Tables for the mean square error test for exact linear restrictions in regression,” Journal of the American Statistical Association 64, 1649–63CrossRef Google Scholar

Zellner, A. (1971), An Introduction to Bayesian Econometrics (New York, Wiley)

Zellner, A. (1984), Basic Issues in Econometrics (Chicago: University of Chicago Press)

Zellner, A. (1986), “On assessing prior distributions and Bayesian regression analysis with g-prior distributions,” in P. K. Goel and A. Zellner (eds.), Bayesian Inference and Decision Techniques: Essays in Honour of Bruno de Finetti (Amsterdam, North-Holland), 233–43

Zellner, A. (1994), “Time series analysis, forecasting, and econometric modelling: the structural econometric modelling, time series analysis (SEMTSA) approach,” Journal of Forecasting 13, 215–33; chapter 4 in this volumeCrossRef Google Scholar

Zellner, A. and Hong, C. (1989), “Forecasting international growth rates using Bayesian shrinkage and other procedures,” Journal of Econometrics, Annals 40, 183–202; chapter 14 in this volumeCrossRef Google Scholar

Zellner, A., Hong, C., and Min, C. (1991), “Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques.” Journal of Econometrics 49, 275–304; chapter 16 in this volumeCrossRef Google Scholar