Box and Cox

Box-Cox Transformation: An Overview Box-Cox Transformations: An Overview

Implementing Box-Cox Quantile Regression∗

The Box-Cox quantile regression model introduced by Powell (1991) is a flexible and numerically attractive extension of linear quantile regression techniques. Chamberlain (1994) and Buchinsky (1995) suggest a two stage estimator for this model but the objective function in stage two of their method may not be defined in an application. We suggest a modification of the estimator which is easy to...

Testing the Box-Cox Parameter for an Integrated Process*

A new approach to the Box–Cox transformation

We propose a new methodology to estimate λ, the parameter of the Box–Cox transformation, as well as an alternative method to determine plausible values for it. The former is accomplished by defining a grid of values for λ and further perform a normality test on the λ-transformed data. The optimum value of λ, say ∗ λ , is such that the p-value from the normality test is the highest. The set of p...

Computing Marginal Effects in the Box–cox Model

This paper considers computation of fitted values and marginal effects in the Box–Cox regression model. Two methods, (1) the ‘‘smearing’’ technique suggested by Duan (see Ref. 10) and (2) direct numerical integration, are examined and compared with the ‘‘naive’’ method often used in econometrics.