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Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods

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Charalambos G. Tsangarides
Alin Mirestean
Huigang Chen

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Abstract

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation results suggest that asymptotically our methodology performs well both in Bayesian model selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.

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Paper provided by International Monetary Fund in its series IMF Working Papers with number 09/74.

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Length: 43 pages
Date of creation: 17 Apr 2009
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Handle: RePEc:imf:imfwpa:09/74

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  3. Ley, Eduardo & Steel, Mark F.J., 2008. "On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression," MPRA Paper 6637, University Library of Munich, Germany, revised 06 Jan 2008. [Downloadable!]
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  4. Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Oxford University Press for Biometrika Trust, vol. 92(1), pages 31-46, March. [Downloadable!] (restricted)
  5. 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. [Downloadable!]
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  6. Enrique Moral-Benito, 2007. "Determinants Of Economic Growth: A Bayesian Panel Data Approach," Working Papers wp2007_0719, CEMFI. [Downloadable!]
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  8. Morales, Knashawn H. & Ibrahim, Joseph G. & Chen, Chien-Jen & Ryan, Louise M., 2006. "Bayesian Model Averaging With Applications to Benchmark Dose Estimation for Arsenic in Drinking Water," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 9-17, March. [Downloadable!] (restricted)
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  10. Koop, Gary & Tole, Lise, 2004. "Measuring the health effects of air pollution: to what extent can we really say that people are dying from bad air?," Journal of Environmental Economics and Management, Elsevier, vol. 47(1), pages 30-54, January. [Downloadable!] (restricted)
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  12. Han Hong & Bruce Preston, 2008. "Bayesian Averaging, Prediction and Nonnested Model Selection," NBER Working Papers 14284, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  13. Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2005. "Fact-Free Learning," American Economic Review, American Economic Association, vol. 95(5), pages 1355-1368, December. [Downloadable!]
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    • Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2004. "Fact-Free Learning," Cowles Foundation Discussion Papers 1491, Cowles Foundation, Yale University. [Downloadable!]
    • Enriqueta Aragones & Itzhak Gilboa & Andrew Postlewaite & David Schmeidler, 2003. "Fact-Free Learning," PIER Working Paper Archive 05-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Dec 2004. [Downloadable!]
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