Probit and logit models typically require a normalization on the error variance for model identification. This paper … shows that in the context of sample mean probability decompositions, error variance normalizations preclude estimation of …

generates discrete choice outcomes. Pregibit nests logit, approximately nests probit, loglog, cloglog and gosset models, and … yields a linear probability model that is solidly founded on the discrete choice framework that underlies logit and probit. …The pregibit discrete choice model is built on a distribution that allows symmetry or asymmetry and thick tails, thin …

generates discrete choice outcomes. Pregibit nests logit, approximately nests probit, loglog, cloglog and gosset models, and … yields a linear probability model that is solidly founded on the discrete choice framework that underlies logit and probit. …The pregibit discrete choice model is built on a distribution that allows symmetry or asymmetry and thick tails, thin …

Discriminant Analysis (MDA) and Probit techniques are employed in the estimation of Z-Score and X-Score models, whereas Logit … consisting of an equal number of defaulted and non-defaulted firms. Out of 208 companies, 130 are used for estimation sample, and … technique is employed in the estimation of Y-Score and the newly proposed models. The performance of all the original, re …

are from a logit or probit model. I describe a relatively simple method of performing a decomposition that uses estimates … from a logit or probit model. Expanding on the original application of the technique in Fairlie (1999), I provide a more …