Variable Selection in Macroeconomic Forecasting with Many Predictors

TL;DR

This paper explores variable selection methods for macroeconomic forecasting with many predictors, comparing their accuracy and prediction performance, and finds some methods outperform factor augment approaches in certain cases.

Contribution

It introduces and evaluates several cutting-edge variable selection techniques in economic forecasting, highlighting the effectiveness of meta-heuristic algorithms and classical methods.

Findings

Meta-heuristic algorithms outperform other methods in accuracy.

Some variable selection methods improve forecasting over factor augment.

Selected predictors align well with economic theories.

Abstract

In the data-rich environment, using many economic predictors to forecast a few key variables has become a new trend in econometrics. The commonly used approach is factor augment (FA) approach. In this paper, we pursue another direction, variable selection (VS) approach, to handle high-dimensional predictors. VS is an active topic in statistics and computer science. However, it does not receive as much attention as FA in economics. This paper introduces several cutting-edge VS methods to economic forecasting, which includes: (1) classical greedy procedures; (2) l1 regularization; (3) gradient descent with sparsification and (4) meta-heuristic algorithms. Comprehensive simulation studies are conducted to compare their variable selection accuracy and prediction performance under different scenarios. Among the reviewed methods, a meta-heuristic algorithm called sequential Monte Carlo algorithm performs the best. Surprisingly the classical forward selection is comparable to it and better than other more sophisticated algorithms. In addition, we apply these VS methods on economic forecasting and compare with the popular FA approach. It turns out for employment rate and CPI inflation, some VS methods can achieve considerable improvement over FA, and the selected predictors can be well explained by economic theories.