Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Model

Abstract

One of the major problems of the empirical economists while building an economic
 model is the selection of variables which should be included in the true regression
 model. Conventional econometrics use several model selection criteria to determine
 the variables. Recent years' developments in Machine Learning (ML) approaches introduced 
 an alternative way to select variables. In this paper, we have an application
 of ML to select variables to include for a nonlinear relationship between inflation and
 economic growth. Among ML methodologies, Random Forest
 approach is one of the most powerful to capture nonlinear relationships. Therefore,
 we applied RF and found that both high and low inflation can be the cause of low
 economic growth which is a major contribution of the paper to economic literature. 
 Moreover, in the paper, as an outcome of RF there are other variables effecting
 economic growth with an order of importance.