On factor models with random missing: EM estimation, inference, and cross validation
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 dist...
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sg-smu-ink.soe_research-32302019-04-19T15:12:18Z On factor models with random missing: EM estimation, inference, and cross validation SU, Liangjun MIAO, Ke JIN, Sainan 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. 2019-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2231 https://ink.library.smu.edu.sg/context/soe_research/article/3230/viewcontent/Factor_with_missing_values20190115_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Cross-validation Expectation-Maximization (EM) algorithm Factor models Matrix completion Missing at random Principal component analysis Singular value decomposition Econometrics |
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Cross-validation Expectation-Maximization (EM) algorithm Factor models Matrix completion Missing at random Principal component analysis Singular value decomposition Econometrics SU, Liangjun MIAO, Ke JIN, Sainan On factor models with random missing: EM estimation, inference, and cross validation |
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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. |
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SU, Liangjun MIAO, Ke JIN, Sainan |
author_facet |
SU, Liangjun MIAO, Ke JIN, Sainan |
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SU, Liangjun |
title |
On factor models with random missing: EM estimation, inference, and cross validation |
title_short |
On factor models with random missing: EM estimation, inference, and cross validation |
title_full |
On factor models with random missing: EM estimation, inference, and cross validation |
title_fullStr |
On factor models with random missing: EM estimation, inference, and cross validation |
title_full_unstemmed |
On factor models with random missing: EM estimation, inference, and cross validation |
title_sort |
on factor models with random missing: em estimation, inference, and cross validation |
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Institutional Knowledge at Singapore Management University |
publishDate |
2019 |
url |
https://ink.library.smu.edu.sg/soe_research/2231 https://ink.library.smu.edu.sg/context/soe_research/article/3230/viewcontent/Factor_with_missing_values20190115_.pdf |
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1770574507419893760 |