publication . Article . 1994

A MONTE CARLO INVESTIGATION OF THE BOX-COX MODEL AND A NONLINEAR LEAST SQUARES ALTERNATIVE

Mark H. Showalter;
Open Access
  • Published: 01 Jan 1994 Journal: Review of Economics & Statistics, volume 76, issue 3 August, pages 560-70
Abstract
This paper reports a Monte Carlo study of the Box-Cox model and a nonlinear least squares alternative. Key results include the following: the transformation parameter in the Box-Cox model appears to be inconsistently estimated in the presence of conditional heteroskedasticity; the constant term in both the Box-Cox and the nonlinear least squares models is poorly estimated in small samples; conditional mean forecasts tend to underestimate their true value in the Box-Cox model when the transformation parameter is not equal to one; and conditional heteroskedasticity tends to worsen the bias in the Box-Cox predicted values. Copyright 1994 by MIT Press.
Subjects
arXiv: Statistics::TheoryStatistics::Methodology
free text keywords: Economics and Econometrics, Social Sciences (miscellaneous), Non-linear least squares, Heteroscedasticity, Conditional variance, Constant term, Econometrics, Monte Carlo method, Transformation parameter, Mathematics, Autoregressive conditional heteroskedasticity, Conditional expectation
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