On the effect of prior assumptions in Bayesian model averaging with applications to growth regresssion
Ley, Eduardo and Steel, Mark F. J.. (2009) On the effect of prior assumptions in Bayesian model averaging with applications to growth regresssion. Journal of Applied Econometrics, Vol.24 (No.4). pp. 651-674. ISSN 0883-7252Full text not available from this repository.
Official URL: http://dx.doi.org/10.1002/jae.1057
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 regressor 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. Copyright (C) 2009 John Wiley & Sons, Ltd.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HB Economic Theory
Q Science > QA Mathematics
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Regression analysis, Bayesian statistical decision theory, Econometrics|
|Journal or Publication Title:||Journal of Applied Econometrics|
|Publisher:||Wiley-Blackwell Publishing, Inc|
|Number of Pages:||24|
|Page Range:||pp. 651-674|
|Access rights to Published version:||Restricted or Subscription Access|
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