An Integrated Panel Data Approach to Modelling Economic Growth (EC)

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. The purpose of this study is to pro- pose an integrated framework that extends the conventional linear growth regression model to allow for parameter heterogeneity and cross-sectional error dependence, while simultaneously performing variable selection by means of a least absolute shrinkage and selection operator estimator. We also derive the asymptotic proper- ties of the estimator under both low and high dimensions, and further investigate the finite sample performance of the estimator through Monte Carlo simulations. We apply the framework to a dataset of 89 countries over the period from 1960 to 2014. Our results broadly support the “optimistic” conclusion of Sala-I-Martin (1997), and also reveal some cross-country patterns not found in previous studies.