  Empirical growth analysis has three major 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
propose 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. We also derive
the asymptotic properties 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 reveal some cross-country
patterns not found in previous studies (e.g., "middle income trap hypothesis",
"natural resources curse hypothesis", "religion works via belief, not
practice", etc.).
