On factor models with random missing: EM estimation, inference, and cross validation

Sainan Jin, Ke Miao and Liangjun Su ()

Journal of Econometrics, 2021, vol. 222, issue 1, 745-777

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 (search for similar items in EconPapers)
JEL-codes: C23 C33 C38 C55 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Working Paper: On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:745-777

DOI: 10.1016/j.jeconom.2020.08.002

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