Using the Quadratic Box-Cox for Flexible Functional Form Selection and Unconditional Variance Computation.

A quadratic Box-Cox methodology is presented for choice of flexible functional form that includes consistent computation of variance estimates. Empirical viability of the procedure is investigated by specifying a dual profit function using highly aggregated U.S. agricultural data. Conditional and unconditional variance estimates for the parameters are compared and contrasted. Likelihood ratio tests are utilized to discriminate among the generalized Leontief, normalized quadratic, translog, and square-rooted quadratic functional forms. Statistical results indicate that the square-rooted quadratic is the preferred choice of functional form for these data, followed by the normalized quadratic.