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

By Alin T Mirestean, Charalambos G Tsangarides, Huigang Chen

April 1, 2009

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Alin T Mirestean, Charalambos G Tsangarides, and Huigang Chen. Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods, (USA: International Monetary Fund, 2009) accessed November 21, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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.

Subject: Bayesian models, Data processing, Estimation techniques

Keywords: Mover accent, WP

Publication Details

Pages:

43
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2009/074
Stock No:

WPIEA2009074
ISBN:

9781451872217
ISSN:

1018-5941

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