Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2021

Abstract

We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross validation-based method to determine the number of factors in factor models with or without missing values and justify its consistency. Simulations demonstrate that our cross validation method is robust to fat tails in the error distribution and significantly outperforms some existing popular methods in terms of correct percentage in determining the number of factors. An application to the factor-augmented regression models shows that a proper treatment of the missing values can improve the out-of-sample forecast of some macroeconomic variables.

Keywords

Cross-validation, Expectation-Maximization (EM) algorithm, Factor models, Matrix completion, Missing at random, Principal component analysis, Singular value decomposition

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

222

Issue

1

First Page

745

Last Page

777

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2020.08.002

Publisher

Elsevier

Embargo Period

7-14-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.jeconom.2020.08.002

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