Determining the number of factors in high-dimensional generalized latent factor models

Yunxiao Chen and Xiaoou Li

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: As a generalization of the classical linear factor model, generalized latent factor models are useful for analysing multivariate data of different types, including binary choices and counts. This paper proposes an information criterion to determine the number of factors in generalized latent factor models. The consistency of the proposed information criterion is established under a high-dimensional setting, where both the sample size and the number of manifest variables grow to infinity, and data may have many missing values. An error bound is established for the parameter estimates, which plays an important role in establishing the consistency of the proposed information criterion. This error bound improves several existing results and may be of independent theoretical interest. We evaluate the proposed method by a simulation study and an application to Eysenck’s personality questionnaire.

Keywords: generalized latent factor model; joint maximum likelihood estimator; high-dimensional data; information criteria; selection consistency (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2022-09-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Biometrika, 1, September, 2022, 109(3), pp. 769 - 782. ISSN: 0006-3444

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:111574

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