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Identification, estimation and testing of conditionally heteroskedastic factor models. (English) Zbl 0977.62111
Summary: 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 arbitrage pricing theory (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.

MSC:
62P05 Applications of statistics to actuarial sciences and financial mathematics
62H25 Factor analysis and principal components; correspondence analysis
62P20 Applications of statistics to economics
91B28 Finance etc. (MSC2000)
91B84 Economic time series analysis
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