Publication Type
Working Paper
Version
publishedVersion
Publication Date
1-2019
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
First Page
1
Last Page
92
Publisher
SMU Economics and Statistics Working Paper Series, No. 04-2019
City or Country
Singapore
Citation
SU, Liangjun; MIAO, Ke; and JIN, Sainan.
On factor models with random missing: EM estimation, inference, and cross validation. (2019). 1-92.
Available at: https://ink.library.smu.edu.sg/soe_research/2231
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.