Efficient Importance Sampling for ML Estimation of SCD Models
CORE Discussion Paper No. 2007/53
31 Pages Posted: 30 Aug 2007
Date Written: August 17, 2007
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