期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2007
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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 EISML
method is easy to apply to extensions of the SCD model.
