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 present a new framework that makes such a joint treatment possible, using flexible nonlinear models specified by Gaussian process priors and addressing the variable selection problem by means of Bayesian model averaging. Using this framework, we extend the linear model to allow for parameter heterogeneity of the type suggested by new growth theory, while taking into account the uncertainty in selecting explanatory variables. Controlling for variable selection uncertainty, we confirm the evidence in favor of parameter heterogeneity presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections.

Gaussian process prior, Growth regression, MCMC, Model averaging, Model uncertainty, Semi-parametric Bayes, Variable selection
Bayesian Analysis (jel C11), Semiparametric and Nonparametric Methods (jel C14), Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Economic Growth and Aggregate Productivity: General (jel O40), Comparative Studies of Countries (jel O57),
Econometric Institute Reprint Series
Journal of Econometrics
Erasmus School of Economics

Salimans, T. (2012). Variable selection and functional form uncertainty in cross-country growth regressions. In Journal of Econometrics (Vol. 171, pp. 267–280). doi:10.1016/j.jeconom.2012.06.007