Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models

Posted: 15 Oct 2001

See all articles by Gabriele Fiorentini

Gabriele Fiorentini

Universita di Firenze - Dipartimento di Statistica

Enrique Sentana

Centro de Estudios Monetarios y Financieros (CEMFI); Financial Markets Group; Centre for Economic Policy Research (CEPR)

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Abstract

We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We show that identification problems are alleviated when variation in factor variances is accounted for. Our results apply to dynamic APT models and other structural models. We also find that traditional ML estimation of unconditional variance parameters remains consistent if the factor loadings are identified from the unconditional distribution, but their standard errors must be robustified. We develop a simple preliminary LM test for ARCH effects in the common factors, and discuss two-step consistent estimation of the conditional variance parameters. Finally, we conduct a detailed simulation exercise.

Keywords: Volatility, Likelihood estimation, APT, Simultaneous equations, Vector autoregressions

JEL Classification: C32

Suggested Citation

Fiorentini, Gabriele and Sentana, Enrique, Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models. Journal of Econometrics, Vol. 102, No. 2, pp. 143-164, June 2001. Available at SSRN: https://ssrn.com/abstract=286552

Gabriele Fiorentini

Universita di Firenze - Dipartimento di Statistica ( email )

Viale Morgagni, 59
50134 Firenze
Italy
+39 055 4237 274 (Phone)
+39 055 4223 560 (Fax)

Enrique Sentana (Contact Author)

Centro de Estudios Monetarios y Financieros (CEMFI) ( email )

Casado del Alisal 5
28014 Madrid
Spain
+34 91 429 0551 (Phone)
+34 91 429 1056 (Fax)

HOME PAGE: http://www.cemfi.es/~sentana/

Financial Markets Group

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London School of Economics & Political Science (LSE)
London WC2A 2AE
United Kingdom
+44 20 7955 7002 (Phone)
+44 20 7852 3580 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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