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Efficient importance sampling for ML estimation of SCD models

Luc Bauwens () and Fausto Galli ()

No 2007053, CORE Discussion Papers from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: The evaluation of the likelihood function of the stochastic conditional duration model requires to compute an integral that has the dimension of the sample size. We apply the efficient importance sampling method for computing this integral. We compare EIS-based ML estimation with QML estimation based on the Kalman filter. We find that EIS-ML estimation is more precise statistically, at a cost of an acceptable loss of quickness of computations. We illustrate this with simulated and real data. We show also that the EIS-ML method is easy to apply to extensions of the SCD model.

Keywords: stochastic conditional duration; importance sampling (search for similar items in EconPapers)
JEL-codes: C13 C15 C41 (search for similar items in EconPapers)
Date: 2007-08-01
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Related works:
Journal Article: Efficient importance sampling for ML estimation of SCD models (2009) Downloads
Working Paper: Efficient importance sampling for ML estimation of SCD models (2009) Downloads
Working Paper: Efficient importance sampling for ML estimation of SCD models (2007) Downloads
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