A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy

TL;DR

This paper proposes a hybrid econometric-machine learning method to measure variable importance, demonstrated through food inflation policy analysis in India, aiming to improve policy prioritization and bridge econometrics with ML.

Contribution

It introduces a novel hybrid approach combining econometrics and machine learning for variable importance analysis in economics and social sciences.

Findings

Effective in prioritizing food policy variables in India

Bridges econometrics and machine learning methodologies

Enhances reliability of importance measures in policy analysis

Abstract

A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the relative stature of explanatory and predictive analysis in economics and the emergence of fruitful collaborations between econometrics and computer science. Learning lessons from both, the author proposes a hybrid approach based on conventional econometrics and advanced machine learning (ML) algorithms, which are otherwise, used in predictive analytics. The purpose of this article is two-fold, to propose a hybrid approach to assess relative importance and demonstrate its applicability in addressing policy priority issues with an example of food inflation in India, followed by a broader aim to introduce the possibility of conflation of ML and conventional econometrics to an audience of researchers in economics and social sciences, in general.