Composite Quantile Factor Model

TL;DR

This paper proposes a composite quantile factor model for high-dimensional panel data, improving factor estimation across different data quantiles, with theoretical properties, simulation validation, and an empirical macroeconomic application.

Contribution

Introduces a novel composite quantile factor model with theoretical distribution results and a new criterion for selecting the number of factors, enhancing factor analysis in high-dimensional data.

Findings

Estimator performs well under various non-normal distributions.

Information criterion accurately determines the number of factors.

Model effectively captures features across multiple quantiles.

Abstract

This paper introduces the method of composite quantile factor model for factor analysis in high-dimensional panel data. We propose to estimate the factors and factor loadings across multiple quantiles of the data, allowing the estimates to better adapt to features of the data at different quantiles while still modeling the mean of the data. We develop the limiting distribution of the estimated factors and factor loadings, and an information criterion for consistent factor number selection is also discussed. Simulations show that the proposed estimator and the information criterion have good finite sample properties for several non-normal distributions under consideration. We also consider an empirical study on the factor analysis for 246 quarterly macroeconomic variables. A companion R package cqrfactor is developed.