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Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods
Author:
Mr. Alin T Mirestean
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Mr. Charalambos G Tsangarides
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, and
Huigang Chen
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Series:
IMF Working Papers
Volume/Issue:
Volume 2009: Issue 074
Language:
English
Publication Date:
01 Apr 2009
DOI:
https://doi.org/10.5089/9781451872217.001
ISBN:
9781451872217
ISSN:
1018-5941
Pages:
43
Keywords:
WP; mover accent
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund
Volume 2009: Issue 074