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

Author/Editor:

Alin T Mirestean ; Charalambos G Tsangarides ; Huigang Chen

Publication Date:

April 1, 2009

Electronic Access:

Free Download. Use the free Adobe Acrobat Reader to view this PDF file

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.

Series:

Working Paper No. 09/74

Subject:

English

Publication Date:

April 1, 2009

ISBN/ISSN:

9781451872217/1018-5941

Stock No:

WPIEA2009074

Format:

Paper

Pages:

43

Please address any questions about this title to publications@imf.org