A Note on the Interpretation of Regression Coefficients within a Class of Truncated Distributions

Abstract

DESPITE THE INTENSE ACTIVITY in the area of estimation of limited dependent variable (LDV) models (see, for example, the Fall 1976 issue of the Annals of Economic and Social Measurement), the interpretation of regression coefficients in truncated regression models has been largely ignored. This issue should not be taken lightly since the obvious interpretation which equates regression coefficients to partial derivatives of the conditional mean of the dependent variable is unfortunately incorrect. One specific implication of the results presented here is that whenever the dependent variable y in the usual classical linear normal regression model is truncated above and/or below, then the effect of the fth regressor on the conditional mean of y is proportional to, but not equal to, the fth regression coefficient. However, more generally this note presents a simple expression for the effect of the fth regressor on any conditional moment of y in terms of a generalized LDV model introduced by Poirier [3]. Poirier introduced a general LDV model which permitted skewness in a pre-truncated variable by transforming it within the class of transformations suggested by Box and Cox