Efficient importance sampling for ML estimation of SCD models

Luc Bauwens and Fausto Galli ()

Computational Statistics & Data Analysis, 2009, vol. 53, issue 6, 1974-1992

Abstract: The evaluation of the likelihood function of the stochastic conditional duration (SCD) model requires to compute an integral that has the dimension of the sample size. ML estimation based on the efficient importance sampling (EIS) method is developed for computing this integral and compared with QML estimation based on the Kalman filter. Based on Monte Carlo experiments, EIS-ML estimation is found to be more precise statistically, but involves an acceptable loss of quickness of computations. The method is illustrated with real data and is shown to be easily applicable to extensions of the SCD model.

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00063-7
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: Efficient importance sampling for ML estimation of SCD models (2009)
Working Paper: Efficient importance sampling for ML estimation of SCD models (2007)
Working Paper: Efficient importance sampling for ML estimation of SCD models (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:6:p:1974-1992

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().