A Monte Carlo Investigation of the Box-Cox Model and a Nonlinear Least Squares Alternative

Mark Showalter ()

The Review of Economics and Statistics, 1994, vol. 76, issue 3, 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.

Date: 1994
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