Sparse Macro Factors

53 Pages Posted: 25 Oct 2018 Last revised: 1 Feb 2021

See all articles by David Rapach

David Rapach

Washington University in St. Louis; Saint Louis University

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: January 31, 2021

Abstract

We use machine-learning techniques to estimate sparse principal components (PCs) for 120 monthly macroeconomic variables from the FRED-MD database. Each sparse PC is a sparse linear combination of the underlying macroeconomic variables, allowing for their economic interpretation. Innovations to the sparse PCs constitute a set of sparse macro factors. Robust tests indicate that sparse macro factors corresponding to yields and housing earn statistically and economically significant risk premia. A three-factor model comprised of the market factor and mimicking portfolio returns for the yields and housing factors performs well compared to leading multifactor models in explaining numerous anomalies.

Keywords: Sparse principal component analysis, FRED-MD, Risk premia, Factor-mimicking portfolio, Three-pass regression, Multifactor models

JEL Classification: C38, C55, C58, E44, G12

Suggested Citation

Rapach, David and Rapach, David and Zhou, Guofu, Sparse Macro Factors (January 31, 2021). Available at SSRN: https://ssrn.com/abstract=3259447 or http://dx.doi.org/10.2139/ssrn.3259447

David Rapach

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Saint Louis University ( email )

3674 Lindell Blvd
St. Louis, MO 63108-3397
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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