A Monte Carlo Investigation of the Box-Cox Model and a Nonlinear Least Squares Alternative
The Review of Economics and Statistics
1994
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.
- DOI
- 10.2307/2109980
- Volume
- 76 (3)
- Pages
- 560
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