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

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

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...

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Detalhes bibliográficos
Autor principal: Mirestean, Alin
Outros Autores: Chen, Huigang, Tsangarides, Charalambos
Formato: Periódico
Idioma:English
Publicado em: Washington, D.C. : International Monetary Fund, 2009.
Colecção:IMF Working Papers; Working Paper ; No. 2009/074
Assuntos:
Mover Accent
WP
Acesso em linha:Full text available on IMF
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245 1 0 |a Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods /  |c Alin Mirestean, Charalambos Tsangarides, Huigang Chen. 
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520 3 |a 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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700 1 |a Chen, Huigang. 
700 1 |a Tsangarides, Charalambos. 
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