IMF Working Papers

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:

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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. 2011/230

Subject:

Bayesian models Estimation techniques Exchange rate arrangements Gravity models