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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

This paper examines 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. The paper analyzes the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors, and predictive performance. The analysis illustrates 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. The results favor particular prior structures for use in this and related contexts.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 4238.

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Date of creation: 01 Jun 2007
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Handle: RePEc:wbk:wbrwps:4238

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Keywords: Educational Technology and Distance Education; Geographical Information Systems; Statistical&Mathematical Sciences; Science Education; Scientific Research&Science Parks;

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  1. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
  2. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
  3. 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.
  4. 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.
  5. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," NBER Working Papers 7750, National Bureau of Economic Research, Inc.
  6. Doppelhofer, G. & Weeks, M., 2005. "Jointness of Growth Determinants," Cambridge Working Papers in Economics 0542, Faculty of Economics, University of Cambridge.
  7. Charalambos G. Tsangarides, 2005. "Growth Empirics under Model Uncertainty: Is Africa Different?," IMF Working Papers 05/18, International Monetary Fund.
  8. William A. Brock & Steven N. Durlauf & Kenneth D. West, 2003. "Policy Evaluation in Uncertain Economic Environments," NBER Working Papers 10025, National Bureau of Economic Research, Inc.
  9. 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.
  10. 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.
  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. Carmen Fernandez & Eduardo Ley & Mark F.J. Steel, 1998. "Benchmark Priors for Bayesian Model Averaging," Econometrics 9804001, EconWPA, revised 31 Jul 1999.
  13. David J. Nott & Robert Kohn, 2005. "Adaptive sampling for Bayesian variable selection," Biometrika, Biometrika Trust, vol. 92(4), pages 747-763, December.
  14. Steven N. Durlauf & Andros Kourtellos & Chih Ming Tan, 2006. "Is God in the Details? A Reexamination of the Role of Religion in Economic Growth," Discussion Papers Series, Department of Economics, Tufts University 0613, Department of Economics, Tufts University.
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