Efficient Importance Sampling for ML Estimation of SCD Models

Bauwens, Luc; Galli, Fausto

doi:10.2139/ssrn.1010651

Efficient Importance Sampling for ML Estimation of SCD Models

CORE Discussion Paper No. 2007/53

31 Pages Posted: 30 Aug 2007

See all articles by Luc Bauwens

Fausto Galli

Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE)

Date Written: August 17, 2007

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

JEL Classification: C13, C15, C41

Suggested Citation: Suggested Citation

Bauwens, Luc and Galli, Fausto, Efficient Importance Sampling for ML Estimation of SCD Models (August 17, 2007). CORE Discussion Paper No. 2007/53, Available at SSRN: https://ssrn.com/abstract=1010651 or http://dx.doi.org/10.2139/ssrn.1010651