Some Alternatives to the Box-Cox Regression Model
Jeffrey Wooldridge
International Economic Review, 1992, vol. 33, issue 4, 935-55
Abstract:
A nonlinear regression model is proposed as an alternative to the Box-Cox regression model for nonnegative variables. The functional form contains linear, exponential, and reciprocal models as special cases. Unlike Box-Cox type approaches, the proposed estimators of the conditional mean function are robust to conditional variance and other distributional misspecifications. Computationally simple, robust Lagrange multiplier statistics for restricted versions of the model are derived. Scale invariant t-statistics are proposed and the Lagrange multiplier statistic for exclusion restrictions is shown to be scale invariant. Copyright 1992 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (32)
Downloads: (external link)
http://links.jstor.org/sici?sici=0020-6598%2819921 ... O%3B2-O&origin=repec full text (application/pdf)
Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.
Related works:
Working Paper: SOME ALETERNATIVE TO THE BOX-COX REGRESSION MODEL (1989)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ier:iecrev:v:33:y:1992:i:4:p:935-55
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0020-6598
Access Statistics for this article
International Economic Review is currently edited by Harold L. Cole
More articles in International Economic Review from Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104-6297. Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and ().