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Pooling in Dynamic Panel-Data Models: An Application to Forecasting GDP Growth Rates

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Author Info

  • Hoogstrate, Andre J
  • Palm, Franz C
  • Pfann, Gerard A

Abstract

In this article, 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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 18 (2000)
Issue (Month): 3 (July)
Pages: 274-83

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Handle: RePEc:bes:jnlbes:v:18:y:2000:i:3:p:274-83

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Cited by:
  1. Simonetta Longhi & Peter Nijkamp, 2005. "Forecasting Regional Labour Market Developments Under Spatial Heterogeneity and Spatial Autocorrelation," Tinbergen Institute Discussion Papers 05-041/3, Tinbergen Institute.
  2. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2007. "A Panel Data Approach to Economic Forecasting: The Bias-Corrected Average Forecast," Economics Working Papers (Ensaios Economicos da EPGE) 650, Graduate School of Economics, Getulio Vargas Foundation (Brazil).
  3. Alev Atak & Oliver Linton & Zhijie Xiao, 2010. "A Semiparametric Panel Model for unbalanced data with Application to Climate Change in the United Kingdom," Boston College Working Papers in Economics 762, Boston College Department of Economics.
  4. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
  5. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
  6. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Series Working Papers 78, University of Oxford, Department of Economics.
  7. Baltagi, Badi H., 2006. "Forecasting with panel data," Discussion Paper Series 1: Economic Studies 2006,25, Deutsche Bundesbank, Research Centre.
  8. Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  9. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2009. "Forecasting with Spatial Panel Data," IZA Discussion Papers 4242, Institute for the Study of Labor (IZA).
  10. Sylvia Kaufmann, 2003. "The business cycle of European countries. Bayesian clustering of country-individual IP growth series," Working Papers 83, Oesterreichische Nationalbank (Austrian Central Bank).

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