PITFALLS OF RESCALING REGRESSION-MODELS WITH BOX-COX-TRANSFORMATIONS
成果类型：
Note
署名作者：
DAGENAIS, MG; DUFOUR, JM
刊物名称：
REVIEW OF ECONOMICS AND STATISTICS
ISSN/ISSBN：
0034-6535
DOI：
10.2307/2109981
发表日期：
1994-08
页码：
571-575
关键词：
摘要：
To facilitate ML estimation for Box-Cox models, several authors have suggested dividing the dependent variable by its sample geometric mean. This paper points out previously unmentioned drawbacks of this ''rescalling.'' First, the ''rescaled'' model is not actually equivalent to the untransformed one, so that the procedure involves more than a unit change. Second, there is no clear interpretation of the parameters after such rescaling. We suggest an interpretation but find that the usual formulas for standard errors and confidence intervals are not asymptotically valid. Only tests for zero coefficients are valid. Thirdly, we discuss the appropriate way of measuring elasticities in such models.
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