Estimation and Testing for Functional Form in First Difference Models
提出一种最大似然方法，用于估计和检验一阶差分回归模型中的正确函数形式，该方法包含简单一阶差分和百分比变化作为特例，并通过三个已发表研究（圣路易斯方程、货币需求模型、贫困与经济增长关系模型）展示其应用。
A maximum likelihood method for estimating and testing for the proper functional form in first difference regression models is developed. The parametric transformation of the regression variables we propose includes simple first differences and percentage changes as special cases. The method has a simple relationship to the familiar Box-Cox test, and the coefficient estimation and LR testing are easily implemented with standard regression packages. We apply the new method to three published studies: the St. Louis equation, a money demand model, and a model relating poverty to economic growth.