Efficient importance sampling for ML estimation of SCD models

@article{Bauwens2009EfficientIS,
  title={Efficient importance sampling for ML estimation of SCD models},
  author={L. Bauwens and F. Galli},
  journal={Computational Statistics & Data Analysis},
  year={2009},
  volume={53},
  pages={1974-1992}
}
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… CONTINUE READING