A flexible bivariate location-scale finite mixture approach to economic growth

TL;DR

This paper proposes a flexible multivariate mixture model for economic growth analysis, allowing for complex heterogeneity and association structures in country growth patterns, extending the augmented Solow model.

Contribution

It introduces a multidimensional discrete distribution for random effects, relaxing unidimensionality, and applies this to detect diverse growth behaviors across countries.

Findings

Evidence of heterogeneous growth patterns among countries

The model captures complex association structures in the data

Simulation results validate the approach under various conditions

Abstract

We introduce a multivariate multidimensional mixed-effects regression model in a finite mixture framework. We relax the usual unidimensionality assumption on the random effects multivariate distribution. Thus, we introduce a multidimensional multivariate discrete distribution for the random terms, with a possibly different number of support points in each univariate profile, allowing for a full association structure. Our approach is motivated by the analysis of economic growth. Accordingly, we define an extended version of the augmented Solow model. Indeed, we allow all model parameters, and not only the mean, to vary according to a regression model. Moreover, we argue that countries do not follow the same growth process, and that a mixture-based approach can provide a natural framework for the detection of similar growth patterns. Our empirical findings provide evidence of heterogenous behaviors and suggest the need of a flexible approach to properly reflect the heterogeneity in the data. We further test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times and levels of heterogeneity in the data.