# An Integrated Panel Data Approach to Modelling Economic Growth

## TL;DR

This paper introduces an integrated panel data framework for economic growth analysis that simultaneously addresses variable selection, parameter heterogeneity, and cross-sectional dependence, providing new insights into cross-country growth patterns.

## Contribution

It extends the linear growth regression model to incorporate heterogeneity and dependence while deriving asymptotic properties and demonstrating finite sample performance.

## Key findings

- Identification of cross-country patterns like the middle income trap
- Evidence supporting the natural resources curse hypothesis
- Insights into how religion influences economic growth

## Abstract

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.).