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 extending the linear model to allow for multiple-regime parameter heterogeneity of the type suggested by new growth theory, while addressing the variable selection problem by means of Bayesian model averaging. Controlling for variable selection uncertainty, we confirm the evidence in favor of new growth theory 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.
|growth regression, model averaging, model uncertainty, 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)|
|Tinbergen Institute Discussion Paper Series|
|Discussion paper / Tinbergen Institute|
Salimans, T. (2011). Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions (No. TI 2011-012/4). Discussion paper / Tinbergen Institute. Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/22337