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

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Author Info

  • Alin Mirestean
  • Charalambos G. Tsangarides
  • Huigang Chen

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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Bibliographic Info

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

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Length: 43
Date of creation: 01 Apr 2009
Date of revision:
Handle: RePEc:imf:imfwpa:09/74

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Related research

Keywords: Economic models; probability; probabilities; econometrics; sample size; equation; dynamic panel; linear regression; dynamic panel data; statistics; bayes factors; random error; equations; dynamic panel data models; hypothesis testing; simulation results; sample sizes; normal distribution; dynamic panels; correlation; monte carlo simulations; random variable; linear regression model; sampling; gamma distribution; bayes factor; sample mean; correlations; nested hypotheses; regression models; cross-country growth regressions; samples; econometric study; number of regressors; bayesian analyses; country growth regressions; calculus; growth regression; covariance; predictions; prediction; regression model; growth regressions; random process; difference equation; factor analysis; linear regression models; bayesian analysis; forecasting; random variables; consistent estimate; linear models; probability distribution; experimental data; cross section analysis;

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References

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Citations

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Cited by:
  1. Ulaşan, Bülent, 2012. "Cross-country growth empirics and model uncertainty: An overview," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 6(16), pages 1-69.
  2. Charalambos G. Tsangarides, 2012. "Determinants of Growth Spells," IMF Working Papers 12/227, International Monetary Fund.
  3. Huigang Chen & Alin Mirestean & Charalambos G. Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 11/230, International Monetary Fund.
  4. Shekhar Aiyar & Romain A Duval & Damien Puy & Yiqun Wu & Longmei Zhang, 2013. "Growth Slowdowns and the Middle-Income Trap," IMF Working Papers 13/71, International Monetary Fund.
  5. Eicher, Theo S. & Helfman, Lindy & Lenkoski, Alex, 2012. "Robust FDI determinants: Bayesian Model Averaging in the presence of selection bias," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 637-651.
  6. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.

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