Abstract
The Box-Cox transformation (BCT) has been frequently used as both a flexible functional form and as a decision device to distinguish among alternative model specifications. Most researchers have failed to recognize that the BCT when applied to the dependent variable can compensate for heteroskedasticity. This paper investigates a new procedure which estimates both the BCT parameters and the analytic form of heteroskedasticity. Results from the new procedure are compared to estimates obtained from the traditional method of estimating BCT models. Comparisons indicate that proper specification of the error variance can influence the magnitude of BCT parameters and alter the results of hypothesis testing.
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