Limited Information Bayesian Model Averaging For Dynamic Panels With An Application To A Trade Gravity Model
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Author | : Huigang Chen |
Publisher | : International Monetary Fund |
Total Pages | : 47 |
Release | : 2011-10-01 |
Genre | : Business & Economics |
ISBN | : 1463921306 |
Download Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model Book in PDF, Epub and Kindle
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.
Author | : Alin Mirestean |
Publisher | : International Monetary Fund |
Total Pages | : 48 |
Release | : 2009-04 |
Genre | : Business & Economics |
ISBN | : |
Download Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods Book in PDF, Epub and Kindle
Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA 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 selection and averaging. In particular, LIBMA recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values. These findings suggest that our methodology is well suited for inference in dynamic panel data models with short time periods in the presence of endogenous regressors under model uncertainty.
Author | : Charalambos G. Tsangarides |
Publisher | : |
Total Pages | : 22 |
Release | : 2006 |
Genre | : |
ISBN | : |
Download A Bayesian Approach to Model Uncertainty Book in PDF, Epub and Kindle
This paper develops the theoretical background for the Limited Information Bayesian Model Averaging (LIBMA). The proposed approach accounts for model uncertainty by averaging over all possible combinations of predictors when making inferences about the variables of interest, and it simultaneously addresses the biases associated with endogenous and omitted variables by incorporating a panel data systems Generalized Method of Moments estimator. Practical applications of the developed methodology are discussed, including testing for the robustness of explanatory variables in the analyses of the determinants of economic growth and poverty.
Author | : Javed Iqbal Ahmed |
Publisher | : |
Total Pages | : 76 |
Release | : 2010 |
Genre | : |
ISBN | : |
Download Prediction Under Model Uncertainty Book in PDF, Epub and Kindle
Author | : Caoxin Sun |
Publisher | : |
Total Pages | : 100 |
Release | : 2018 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Bayesian Model Averaging of Space Time CAR Models with Application to U.S. House Price Foresasting Book in PDF, Epub and Kindle
Author | : Rodney W. Strachan |
Publisher | : |
Total Pages | : 50 |
Release | : 2008 |
Genre | : |
ISBN | : |
Download Bayesian Averaging Over Many Dynamic Model Structures with Evidence on the Great Ratios and Liquidity Trap Risk Book in PDF, Epub and Kindle
Author | : Enrique Moral-Benito |
Publisher | : |
Total Pages | : 55 |
Release | : 2011 |
Genre | : |
ISBN | : |
Download Dynamic Panels with Predetermined Regressors Book in PDF, Epub and Kindle
Author | : Eduardo Ley |
Publisher | : |
Total Pages | : |
Release | : 2011 |
Genre | : |
ISBN | : |
Download Mixtures of G-priors for Bayesian Model Averaging with Economic Application Book in PDF, Epub and Kindle
Author | : Eduardo Ley |
Publisher | : |
Total Pages | : |
Release | : 2009 |
Genre | : |
ISBN | : |
Download On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression Book in PDF, Epub and Kindle
We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross-country growth regressions using three datasets with 41-67 potential drivers of growth and 72-93 observations. Finally, we recommend priors for use in this and related contexts.
Author | : Michael Declan Power |
Publisher | : |
Total Pages | : 56 |
Release | : 2020 |
Genre | : |
ISBN | : |
Download Bayesian Model Averaging Sufficient Dimension Reduction Book in PDF, Epub and Kindle
In sufficient dimension reduction (Li, 1991; Cook, 1998b), original predictors are replaced by their low-dimensional linear combinations while preserving all of the conditional information of the response given the predictors. Sliced inverse regression [SIR; Li, 1991] and principal Hessian directions [PHD; Li, 1992] are two popular sufficient dimension reduction methods, and both SIR and PHD estimators involve all of the original predictor variables. To deal with the cases when the linear combinations involve only a subset of the original predictors, we propose a Bayesian model averaging (Raftery et al., 1997) approach to achieve sparse sufficient dimension reduction. We extend both SIR and PHD under the Bayesian framework. The superior performance of the proposed methods is demonstrated through extensive numerical studies as well as a real data analysis.