Resumen de Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models.Versión Revisada

Enrique Sentana Iváñez, Gabriele Fiorentini

• We investigate several important inference issues for factor models with dynamic heteroskedasticity in the common factors. First, we show that such models are identifed 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 auto regressions. Secondly, we propose a consistent two-step estimation procedure which does 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 Carlo analysis of the finite sample properties of the proposed estimators and hypothesis tests.