On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression

Ley, Eduardo and Steel, Mark F.J. (2008): On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression. Published in: Journal of Applied Econometrics , Vol. 4, No. 24 (2009): pp. 651-674.

Abstract

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 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts.

• Item Type: MPRA Paper
• Institution: The World Bank
• Language: English
• Keywords: Model size; Model uncertainty; Posterior odds; Prediction; Prior odds; Robustness
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence

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