Bootstrap prediction intervals for autoregressive conditional duration models. Issue 15 (13th October 2019)

• Record Type: Journal Article
• Authors: Pokhriyal, H.; Balakrishna, N.
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 15(2019)
• Page Start: 2930
• Page End: 2950
• Publication Date: 2019-10-13
• Subjects: ACD models -- conditional least squares -- maximum likelihood estimation -- quasi-maximum likelihood estimation -- re-sampling; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1644513
• Languages: English
• ISSNs: 0094-9655

Abstract

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.