Model uncertainty in cross‐country growth regressions

Citations Over TimeTop 1% of 2001 papers

Abstract

Abstract We investigate the issue of model uncertainty in cross‐country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is spread widely among many models, suggesting the superiority of BMA over choosing any single model. Out‐of‐sample predictive results support this claim. In contrast to Levine and Renelt ( 1992 ), our results broadly support the more ‘optimistic’ conclusion of Sala‐i‐Martin ( 1997b ), namely that some variables are important regressors for explaining cross‐country growth patterns. However, care should be taken in the methodology employed. The approach proposed here is firmly grounded in statistical theory and immediately leads to posterior and predictive inference. Copyright © 2001 John Wiley & Sons, Ltd.

Related Papers

• → Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework(2019)16 cited
• → Theories of inference or simple additives(1981)17 cited
• → Exploration and Inference(1985)
• → Exact Statistical Inference for the Wasserstein Distance by Selective Inference(2021)
• → Exact Statistical Inference for the Wasserstein Distance by Selective\n Inference(2021)