Improved penalization for determining the number of factors in approximate factor models

L. Alessi; M. Barigozzi; M. Capasso

doi:10.1016/j.spl.2010.08.005

Improved penalization for determining the number of factors in approximate factor models

L. Alessi, M. Barigozzi^*, M. Capasso

^*Corresponding author for this work

Macro, International & Labour Economics

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

The procedure proposed by Bai and Ng (2002) for identifying the number of factors in static factor models is revisited. In order to improve its performance, we introduce a tuning multiplicative constant in the penalty, an idea that was proposed by Hallin and Liska (2007) in the context of dynamic factor models. Simulations show that our method in general delivers more reliable estimates, in particular in the case of large idiosyncratic disturbances.

Original language	English
Pages (from-to)	1806-1813
Number of pages	8
Journal	Statistics & Probability Letters
Volume	80
Issue number	23-24
DOIs	https://doi.org/10.1016/j.spl.2010.08.005
Publication status	Published - 1 Dec 2010

Keywords

Number of factors
Approximate factor models
Information criterion
Model selection
DYNAMIC-FACTOR MODEL

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10.1016/j.spl.2010.08.005

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abstract = "The procedure proposed by Bai and Ng (2002) for identifying the number of factors in static factor models is revisited. In order to improve its performance, we introduce a tuning multiplicative constant in the penalty, an idea that was proposed by Hallin and Liska (2007) in the context of dynamic factor models. Simulations show that our method in general delivers more reliable estimates, in particular in the case of large idiosyncratic disturbances.",

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AB - The procedure proposed by Bai and Ng (2002) for identifying the number of factors in static factor models is revisited. In order to improve its performance, we introduce a tuning multiplicative constant in the penalty, an idea that was proposed by Hallin and Liska (2007) in the context of dynamic factor models. Simulations show that our method in general delivers more reliable estimates, in particular in the case of large idiosyncratic disturbances.

KW - Number of factors

KW - Approximate factor models

KW - Information criterion

KW - Model selection

KW - DYNAMIC-FACTOR MODEL

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DO - 10.1016/j.spl.2010.08.005

M3 - Article

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JO - Statistics & Probability Letters

JF - Statistics & Probability Letters

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ER -

Improved penalization for determining the number of factors in approximate factor models

Abstract

Keywords

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