On the penalized maximum likelihood estimation of high-dimensional approximate factor model

In this paper, we mainly focus on the penalized maximum likelihood estimation of the high-dimensional approximate factor model. Since the current estimation procedure can not guarantee the positive definiteness of the error covariance matrix, by reformulating the estimation of error covariance matri...

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Veröffentlicht in:Computational statistics Jg. 34; H. 2; S. 819 - 846
Hauptverfasser: Wang, Shaoxin, Yang, Hu, Yao, Chaoli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019
Springer Nature B.V
Schlagworte:
Algorithms
Computer simulation
Covariance matrix
Data analysis
Economic models
Economic Theory/Quantitative Economics/Mathematical Methods
Errors
Estimating techniques
Mathematical analysis
Mathematics and Statistics
Matrix methods
Maximum likelihood estimation
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Statistics
EM precedure
Accelerated proximal gradient algorithm
Error covariance matrix
Positive definiteness
ISSN:0943-4062, 1613-9658
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Zusammenfassung:In this paper, we mainly focus on the penalized maximum likelihood estimation of the high-dimensional approximate factor model. Since the current estimation procedure can not guarantee the positive definiteness of the error covariance matrix, by reformulating the estimation of error covariance matrix and based on the lagrangian duality, we propose an accelerated proximal gradient (APG) algorithm to give a positive definite estimate of the error covariance matrix. Combined the APG algorithm with EM method, a new estimation procedure is proposed to estimate the high-dimensional approximate factor model. The new method not only gives positive definite estimate of error covariance matrix but also improves the efficiency of estimation for the high-dimensional approximate factor model. Although the proposed algorithm can not guarantee a global unique solution, it enjoys a desirable non-increasing property. The efficiency of the new algorithm on estimation and forecasting is also investigated via simulation and real data analysis.
Bibliographie:ObjectType-Article-1
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ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-019-00869-z