Identification, estimation and testing of conditionally heteroskedastic factor models

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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.
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