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

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Author Info

  • Huigang Chen
  • Alin Mirestean
  • Charalambos G. 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.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 11/230.

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Length: 45
Date of creation: 01 Oct 2011
Date of revision:
Handle: RePEc:imf:imfwpa:11/230

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Related research

Keywords: Bilateral trade; Economic models; probability; probabilities; econometrics; equation; sample size; linear regression; dynamic panel; statistics; dynamic panel data; random error; bayes factors; difference equation; dynamic panel data models; normal distribution; dynamic panels; equations; linear regression model; hypothesis testing; simulation results; regression models; sample sizes; correlation; time series; number of regressors; monte carlo simulations; growth regressions; linear regression models; random variable; sample mean; gamma distribution; standard errors; bayesian analysis; sampling; statistical significance; monte carlo simulation; bayes factor; dynamic panel model; regression model; samples; bayesian analyses; correlations; estimation method; nested hypotheses; country growth regressions; econometric study; cross-country growth regressions; standard deviations; markov chain; dummy variable; gaussian distribution; prediction; central limit theorem; experimental data; computation; stata; random process; factor analysis; covariance; monte carlo methods; probability distribution; consistent estimate; missing observations; statistical theory; random variables; parameter estimate; bayesian information criterion; difference ? equation; cross section analysis; forecasting;

This paper has been announced in the following NEP Reports:

References

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Cited by:
  1. Douglas Campbell, 2012. "Estimating the Impact of Currency Unions on Trade Using a Dynamic Gravity Framework," Working Papers 121, University of California, Davis, Department of Economics.
  2. Roberto Leon-Gonzalez & Daniel Montolio, 2012. "Endogeneity and Panel Data in Growth Regressions: A Bayesian Model Averaging Approach," GRIPS Discussion Papers 12-08, National Graduate Institute for Policy Studies.

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