Tests for serial dependence in static, non-Gaussian factor models

Abstract

Abstract Here, the simple algebraic expressions are derived for score tests of serial correlation in the levels and squares of common and idiosyncratic factors in static factor models with (semi) parametrically specified elliptical distributions even though one must generally compute the likelihood by simulation. The chapter also robustifies the Gaussian tests against non-normality. The orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices reflect their unobservability. The Monte Carlo exercises assess the finite sample reliability and power of the proposed tests, and compare them to other existing procedures. Finally, the methods are applied to monthly U.S. stock returns.

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