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

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Author Info
Showalter, Mark H

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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.

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Publisher Info
Article provided by MIT Press in its journal Review of Economics & Statistics.

Volume (Year): 76 (1994)
Issue (Month): 3 (August)
Pages: 560-70
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Handle: RePEc:tpr:restat:v:76:y:1994:i:3:p:560-70

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  1. Jason Abrevaya, 2002. "Computing Marginal Effects In The Box-Cox Model," Econometric Reviews, Taylor and Francis Journals, vol. 21(3), pages 383-393. [Downloadable!] (restricted)
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