Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model

Author/Editor:

Huigang Chen ; Alin T Mirestean ; Charalambos G Tsangarides

Publication Date:

October 1, 2011

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:

This paper extends the Bayesian Model Averaging 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 averaging and selection. In particular, LIBMA recovers the data generating process well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to their true values. These findings suggest that our methodology is well suited for inference in short dynamic panel data models with endogenous regressors in the context of model uncertainty. We illustrate the use of LIBMA in an application to the estimation of a dynamic gravity model for bilateral trade.

Series:

Working Paper No. 11/230

English

Publication Date:

October 1, 2011

ISBN/ISSN:

9781463921309/1018-5941

Stock No:

WPIEA2011230

Format:

Paper

Pages:

45

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