Binary Response Forecasting under a Factor-Augmented Framework

TL;DR

This paper introduces a factor-augmented binary response forecasting model that leverages latent factors for improved recession prediction, demonstrating superior performance over traditional methods in simulations and real data.

Contribution

It develops a maximum likelihood estimation approach for the new model and establishes its asymptotic properties, advancing binary response forecasting techniques.

Findings

The proposed method outperforms Probit regression in recession forecasting.

Monte Carlo simulations confirm strong finite-sample performance.

The model effectively utilizes high-dimensional information via latent factors.

Abstract

In this paper, we propose a novel factor-augmented forecasting regression model with a binary response variable. We develop a maximum likelihood estimation method for the regression parameters and establish the asymptotic properties of the resulting estimators. Monte Carlo simulation results show that the proposed estimation method performs very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to U.S. recession forecasting. The proposed model consistently outperforms conventional Probit regression across both in-sample and out-of-sample exercises, by effectively utilizing high-dimensional information through latent factors.