On factor models with random missing: EM estimation, inference, and cross validation
By Sainan Jin, Ke Miao, Liangjun Su. 2020
55 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/eadaf904382030d3a1b03bf346fdf5bc7741609f
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Citations in the corpus
- Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data (2019, background): “Our analysis is similar in objective to that of Su et al. (2019) and Xiong and Pelger (2019) but differs in two ways. First, our results hold for arbitrary type of missing data and does not require assumptions about the missing data mechanism. Second, the estimates produced by our algorithms are con”