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

Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods by Alin Mirestean
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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.
International Monetary Fund; April 2009
43 pages; ISBN 9781452712741
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Title: Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods
Author: Alin Mirestean; Charalambos G. Tsangarides; Huigang Chen
 
ISBNs
1451916566
9781451872217
9781451916560
9781452712741