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Identification, estimation and testing of conditionally heteroskedastic factor models

Gabriele Fiorentini () and Enrique Sentana ()

Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)

Abstract: We investigate several important inference issues for factor models with dynamic heteroskedasticity in the common factors. First, we show that such models are identified if we take into account the time-variation in the variances of the factors. Our results also apply to dynamic versions of the APT, dynamic factor models, and vector autoregressions.Secondly, we propose a consistent two-step estimation procedure which do es not rely on knowledge of any factor estimates, and explain how to compute correct standard errors.Thirdly, we develop a simple preliminary LM test for the presence of ARCH effects in the common factors. Finally, we conduct a Monte CarIo analysis of the finite sample properties of the proposed estimators and hypothesis tests.

Keywords: Factor Models; Conditional Heteroskedasticity; Identification (search for similar items in EconPapers)
Pages: 48 pages
Date: 1997-10
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Published by Ivie

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http://www.ivie.es/downloads/docs/wpasad/wpasad-1997-22.pdf Fisrt version / Primera version, 1997 (application/pdf)

Related works:
Journal Article: Identification, estimation and testing of conditionally heteroskedastic factor models (2001) Downloads
Working Paper: Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Model (1997)
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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:1997-22

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