Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2008
Abstract
The authors propose a simple but general method of inference for a parametric function of the Box-Cox-type transformation model. Their approach is built upon the classical normal theory but takes parameter estimation into account. It quickly leads to test statistics and confidence intervals for a linear combination of scaled or unscaled regression coefficients, as well as for the survivor function and marginal effects on the median or other quantile functions of an original response. The authors show through simulations that the finite-sample performance of their method is often superior to the delta method, and that their approach is robust to mild departures from normality of error distributions. They illustrate their approach with a numerical example.
Keywords
Box-Cox transformation, confidence interval, marginal effect, percentile function, robustnesssurvivor function, test, variance inflation factor
Discipline
Econometrics
Research Areas
Econometrics
Publication
Canadian Journal of Statistics
Volume
36
Issue
2
First Page
301
Last Page
319
ISSN
0319-5724
Identifier
10.1002/cjs.5550360208
Publisher
Statistical Science Association of Canada
Citation
YANG, Zhenlin; WU, Eden Ka-Ho; and DESMOND, Anthony F..
Inference for General Parametric Functions in Box-Cox-Type Transformation Models. (2008). Canadian Journal of Statistics. 36, (2), 301-319.
Available at: https://ink.library.smu.edu.sg/soe_research/297
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/10.1002/cjs.5550360208