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On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression

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  • Ley, Eduardo
  • Steel, Mark F.J.

Abstract

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross-country growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 6773.

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Date of creation: 06 Jan 2008
Date of revision: 06 Jan 2008
Publication status: Published in Journal of Applied Econometrics 24.4(2009): pp. 651-674
Handle: RePEc:pra:mprapa:6773

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Keywords: Model size; Model uncertainty; Posterior odds; Prediction; Prior odds; Robustness;

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  1. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  2. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
  3. Carmen Fernández & Eduardo Ley & Mark F. J. Steel, . "Benchmark priors for Bayesian Model averaging," Working Papers 98-06, FEDEA.
  4. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
  5. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
  6. David J. Nott & Robert Kohn, 2005. "Adaptive sampling for Bayesian variable selection," Biometrika, Biometrika Trust, vol. 92(4), pages 747-763, December.
  7. Ley, Eduardo & Steel, Mark F. J., 2006. "Jointness in Bayesian variable selection with applications to growth regression," Policy Research Working Paper Series 4063, The World Bank.
  8. Steven N. Durlauf & Andros Kourtellos & Chih Ming Tan, 2012. "Is God in the details? A reexamination of the role of religion in economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1059-1075, November.
  9. Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers 90-92-23, California Irvine - School of Social Sciences.
  10. Brock,W.A. & Durlauf,S.N. & West,K.D., 2003. "Policy evaluation in uncertain economic environments," Working papers 15, Wisconsin Madison - Social Systems.
  11. Chris Papageorgiou & Winford H. Masanjala, . "Initial Conditions, European Colonialism and Africa's Growth," Departmental Working Papers 2006-01, Department of Economics, Louisiana State University.
  12. Charalambos G. Tsangarides, 2005. "Growth Empirics Under Model Uncertainty," IMF Working Papers 05/18, International Monetary Fund.
  13. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
  14. Theo Eicher & Chris Papageogiou & Adrian E Raftery, 2007. "Default Priors and Predictive Performance in Bayesian Model Averaging, with Application to Growth Determinants," Working Papers UWEC-2007-25-P, University of Washington, Department of Economics.
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