Abstract
Empirical growth analysis is plagued with three problems – variable selection, parameter heterogeneity and cross-sectional dependence – which are addressed independently from each other in most studies. This study is to propose an integrated framework that allows for parameter heterogeneity and cross-sectional error dependence, while simultaneously performing variable selection. We derive the asymptotic properties of the estimator, and apply the framework to a dataset of 89 countries over the period from 1960 to 2014. Our results support the “optimistic” conclusion of Sala-I-Martin (1997), and also reveal some cross-country patterns not found previously.
Highlights
Following the seminal works of Kormendi and Meguire (1985) and Barro (1991), a vast amount of studies in the empirical growth literature have attempted to identify salient determinants of economic growth
The main goal of this study is to propose an integrated framework that is capable of dealing with parameter heterogeneity and cross-sectional dependence, while simultaneously performing variable selection
A rigorous cross-country growth regression analysis should simultaneously account for three major problems identified in the literature — variable selection, parameter heterogeneity, and cross-sectional dependence
Summary
Following the seminal works of Kormendi and Meguire (1985) and Barro (1991), a vast amount of studies in the empirical growth literature have attempted to identify salient determinants of economic growth. This problem stems from the fact that the number of potential explanatory variables is large (over 140 identified in Durlauf et al, 2005) relative to the number of countries with enough data availability, rendering the all-inclusive regression computationally infeasible (Sala-I-Martin et al, 2004; Durlauf et al, 2005). In dealing with this problem some studies have resorted to “trying” combinations of variables which could be potentially important determinants of growth and report the results of their preferred specification. As noted by Leamer (1983) and Sala-I-Martin et al (2004) such “data-mining” could lead to spurious inference
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