Use the data in LOANAPP for this exercise. The binary variable to be explained is approve, which is equal to one if a mortgage loan to an individual was approved. The key explanatory variable is white, a dummy variable equal to one if the applicant was white. The other applicants in the data set are black and Hispanic. To test for discrimination in the mortgage loan market, a linear probability model can be used: approve =β0 β1 white other factors. (i) If there is d

Econometrics Academy - Probit and Logit Models

The following table contains the parameter estimates of the linear probability regression model and the logistic regression model when considering binary response variable y and two predictor variables X1 and X2. What is the predicted probability implied by the logistic regression model for X1 = 92 and X2 = 157? (Hint: for logit, the model is exp(b0 b1*X1 b2*X2)/(1 exp(b0 b1*X1 b2*X2)))Linear Variable Probability Logistic Intercept -3.40 1.11 0.18

Consider a model with a zero-one dependent variable. You have a multiple regression program and a program for the logit and probit models. You have computed the coefficients of the linear probability model and the logit and probit models: (a) How will you transform the coefficients of the three models so that they are comparable? (b) How will you compute the R^2 's for the three models? (c) By what criteria will you choose the best model? | Numerade