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Project Euclid (Hosted at Cornell University Library)
Descripción: This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification restrictions. Unlike the principal components approach, the maximum likelihood estimator explicitly allows heteroskedasticities, which are jointly estimated with other parameters. Efficiency of MLE relative to the principal components method is also considered.
Autor(es): Bai, Jushan - Li, Kunpeng -
Id.: 55202789
Idioma:
inglés
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Versión: 1.0
Estado: Final
Tipo: application/pdf -
Palabras clave: High - dimensional factor models -
Tipo de recurso:
Text
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Tipo de Interactividad: Expositivo
Nivel de Interactividad: muy bajo
Audiencia:
Estudiante
- Profesor
- Autor
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Estructura: Atomic
Coste: no
Copyright: sí
: Copyright 2012 Institute of Mathematical Statistics
Formatos: application/pdf -
Requerimientos técnicos: Browser: Any -
Relación:
[References] 0090-5364
Fecha de contribución: 15-may-2012
Contacto:
Localización:
* doi:10.1214/11-AOS966
