Tests for Serial Dependence in Static, Non-Gaussian Factor Models
Gabriele Fiorentini () and
Enrique Sentana ()
Working Papers from CEMFI
We derive simple algebraic expressions 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. We also robustify our Gaussian tests against nonnormality. The orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices reflect their unobservability. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests, and compare them to other existing procedures. Finally, we apply our methods to monthly US stock returns.
Keywords: ARCH; Financial returns; Kalman filter; LM tests; Non-Gaussian state space models; Predictability. (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 C32 C38 C46 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:cmf:wpaper:wp2012_1211
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