Publication Type
Journal Article
Version
submittedVersion
Publication Date
12-2004
Abstract
This paper proposes a unified approach to constructing confidence limits for a future percentile duration or event-time. The construction is based on an analytical calibration of the Box-Cox-type “plug-in” percentile limits (PL). The performance of the calibrated Box-Cox PL is investigated using Monte Carlo experiments. Comparisons are made with PLs that are specifically designed for a particular distribution such as Weibull and lognormal. Excellent performances of the calibrated Box-Cox PL are observed. Simulation based on other popular duration models such as gamma and inverse Gaussian reveal that the proposed PL is robust against distributional assumptions and that it performs much better than the distribution-free PL. An empirical illustration is also provided.
Keywords
Analytical calibration, Box-Cox transformation, Duration model, Event-time model, Percentile limits
Discipline
Econometrics
Research Areas
Econometrics
Publication
Insurance: Mathematics and Economics
Volume
35
Issue
3
First Page
649
Last Page
677
ISSN
0167-6687
Identifier
10.1016/j.insmatheco.2004.08.002
Publisher
Elsevier
Citation
YANG, Zhenlin and TSUI, Albert K.C..
Analytically Calibrated Box-Cox Percentile Limits for Duration and Event-Time Models. (2004). Insurance: Mathematics and Economics. 35, (3), 649-677.
Available at: https://ink.library.smu.edu.sg/soe_research/191
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.insmatheco.2004.08.002