Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions

Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions

Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We perform this joint treatment by extend...

Model Uncertainty in Cross-country Growth Regressions

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results support this claim. In contrast with Levine and Renelt (1992), our results broadly support the more “optim...

Dualism and cross-country growth regressions

This paper examines whether growth regressions should incorporate dualism and structural change. If there is a differential across sectors in the marginal product of labour, changes in the structure of employment can raise aggregate total factor productivity. The paper develops empirical growth models that allow for this effect in a more flexible way than previous work. Estimates of the models ...

Human Capital and Growth in Cross-Country Regressions

Truth and Robustness in Cross-country Growth Regressions

The work of Levine and Renelt (1992) and Sala-i-Martin (1997a, b) which attempted to test the robustness of various determinants of growth rates of per capita GDP among countries using two variants of Edward Leamer’s extreme-bounds analysis is reexamined. In a realistic Monte Carlo experiment in which the universe of potential determinants is drawn from those in Levine and Renelt’s study, both ...