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Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model, Huigang Chen, Alin Mirestean, Charalambos Tsangarides

Abstract
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
Table Of Contents
Cover Page; Title Page; Copyright Page; Contents; I. Introduction; II. Model Uncertainty in the Bayesian Context; A. Model Selection and Hypothesis Testing; B. Bayesian Model Averaging; C. Choice of Priors; III. Limited Information Bayesian Model Averaging; A. A Dynamic Panel Data Model with Endogenous Regressors; B. Estimation and Moment Conditions; C. The Limited Information Criterion; IV. Monte Carlo Simulation and Results; A. The Data Generating Process; B. Simulation Results; 1. Model Selection; 2. Model Averaging; 3. Robustness Checks Using non-Gaussian Errors
Language
eng
Literary Form
non fiction
Note
Description based upon print version of record
Physical Description
1 online resource (62 p.)
Specific Material Designation
remote
Form Of Item
online
Isbn
9781463967161

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