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Bootstrap prediction intervals for autoregressive conditional duration models.
- Source :
-
Journal of Statistical Computation & Simulation . Oct2019, Vol. 89 Issue 15, p2930-2950. 21p. - Publication Year :
- 2019
-
Abstract
- In the recent past, the autoregressive conditional duration (ACD) models have gained popularity in modelling the durations between successive events. The aim of this paper is to propose a simple and distribution free re-sampling procedure for developing the forecast intervals of linear ACD Models. We use the conditional least squares method to estimate the parameters of the ACD Model instead of the conditional Maximum Likelihood Estimation or Quasi-Maximum Likelihood Estimation and show that they are consistent for large samples. The properties of the proposed procedure are illustrated by a simulation study and an application to two real data sets. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MAXIMUM likelihood statistics
Subjects
Details
- Language :
- English
- ISSN :
- 00949655
- Volume :
- 89
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Journal of Statistical Computation & Simulation
- Publication Type :
- Academic Journal
- Accession number :
- 138026992
- Full Text :
- https://doi.org/10.1080/00949655.2019.1644513