Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model
![WPIEA2011230 Image](images/WP.jpg)
Author/Editor: Huigang Chen, Alin Mirestean, Charalambos G. Tsangarides
Release Date: © October, 2011
ISBN
: 978-1-46392-130-9
Stock #: WPIEA2011230
English
Stock Status: On back-order
Languages and formats available
English | French | Spanish | Arabic | Russian | Chinese | Portuguese | |
Paperback | Yes | ||||||
Yes | |||||||
ePub | Yes | ||||||
Mobipocket | Yes |
Description
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.
More publications in this series: Working Papers
More publications by: Huigang Chen ; Alin Mirestean ; Charalambos G. Tsangarides