clear all set more off use C:\Data\probit_insurance * Using Global statements to reduce the burden of continually entering * target and independent variables in STATA command statements global ylist ins global xlist retire age hstatusg hhincome educyear married hisp describe \$ylist \$xlist summarize \$ylist \$xlist tabulate \$ylist * Linear Probability Model with Robust Standard Errors reg \$ylist \$xlist, vce(robust) * Calculate Marginal Effects at Means and the Average Marginal Effects * Notice that coefficient by coefficient the LS coefficients exactly * match the marginal effects measured at the means and the average of * the marginal effects as in the Average marginal probability effects * (AMPEs) as calculated in equation (4.10) in the W&B textbook. quietly reg \$ylist \$xlist margins, dydx(*) atmeans margins, dydx(*) * Properties of marginal effects in both Probit and Logit Models: * (1) The sign of the marginal effect is the same as the sign of * the coefficient * (2) The marginal effect is largest at x(transpose)*beta = 0 * (3) The marginal effect varies by individual * Probit model probit \$ylist \$xlist * Calculate Marginal Effects quietly probit \$ylist \$xlist margins, dydx(*) atmeans margins, dydx(*) * Plot the Receiver Operating Characteristic (ROC) Curve lroc \$ylist * Report the Classification Table Results for the Probit Model * using a cutoff probability of 0.3871 which is the proportion * of people in the sample that have insurance estat classification , cutoff(0.3871) * Plot the Sensitivity and Specificity of the Probit Model * as a function of the cut-off probability lsens \$ylist * One of the nice things about the Logit model is that * not only can the coeficients be reported but if one uses * the "or" option the Odd ratio estimates for each coefficient * can be reported. For example, if the odds ratio = exp(b) * of a coefficient is 0.5, then for a one unit increase in * the associated variable, the odds ratio (= p/(1-p)) goes * down by 50%. If the odds ratio is 1.0 then a one unit * increase in the associated variable does not change the odds * ratio. If the odds ratio is 1.5 then a one unit increase * in the associated variable increase the odds ratio by 50% * Notice that if the odds ratio is significantly different from * then the corresponding logit coefficient will be significantly * different from zero * Logit model logit \$ylist \$xlist * Reporting the odds ratio using the "or" option in the logit command logit \$ylist \$xlist, or * Plot the Receiver Operating Characteristic (ROC) Curve lroc \$ylist * Report the Classification Table Results for the Logit Model * using a cutoff probability of 0.3871 which is the proportion * of people in the sample that have insurance estat classification , cutoff(0.3871) * Plot the Sensitivity and Specificity of the Probit Model * as a function of the cut-off probability lsens \$ylist ******************************************************** * Now to calculate Predicted Probabilities quietly logit \$ylist \$xlist predict plogit, pr quietly probit \$ylist \$xlist predict pprobit, pr quietly regress \$ylist \$xlist predict pols, xb * Now we summarize the predicted probabilities * of the three different methods: Probit, Logit, and LPM summarize \$ylist plogit pprobit pols