A Useful Interpretation of RSquared in Binary Choice Models (Or, Have We Dismissed the Good Old RSquared; Prematurely)
Author  

Keywords  
Abstract 
The discreditation of the Linear Probability Model (LPM) has led to the dismissal of the standard \(R^{2}\) as a measure of goodnessofﬁt in binary choice models. It is argued that as a descriptive tool the standard \(R^{2}\) is still superior to the measures currently in use. In the LPM model \(R^{2}\) has a simple interpretation: it equals the difference between the average predicted probability in the two groups. It also measures the fraction of the explained part of the variance (SSR) due to the difference between the conditional means (SSB). Given \(R^{2}\) and the sample proportion \(P\) one can calculate the conditional means, \(\bar{P}_{0}\) and \(\bar{P}_{1}\). This interpretation still holds for nonlinear cases when \(R\) is computed as the regression coefﬁcient of the predicted value on the dependent binary variable: However, even if other deﬁnitions of \(R^{2}\) are used in this case (e. g., the share of the variance explained by the regression, or the correlation coefficient between true and predicted values), the measure is very close to \(\bar{P}_{1}  \bar{P}_{0}\). 
Year of Publication 
1998

Number 
397

Date Published 
02/1998

Publication Language 
eng

Citation Key 
7857

URL  
Working Papers
