* Linear Probability Model for House Ownership * * Keywords: * regression, weighted least squares, heteroskedasticity, probability, * house ownership * * Description: * We illustrate how to estimate a Linear Probability Model for House * Ownership and apply a heteroskedasticity correction * * Author(s): * Skif Pankov * * Source: * Damodar N. Gujarati and Dawn C. Porter, Basic Econometrics - 5th Edition * McGraw-Hill International Edition, Chapter 15, Example 15.1 (page 547) * sample 1 40 * Reading the datafile and naming variables read (data_15.1.shd) y x * Running an OLS regression of y on x, stating to save the predicted * values in a variable yhat ols y x / predict = yhat * Excluding datapoint for which the predicted probability values (yhat) * are either negative or greater than one - this is done by generating * new variables y1 and x1 genr y1 = y genr x1 = x skipif (yhat.ge.1).or.(yhat.le.0) * Generating weights w genr w = 1 / (yhat*(1-yhat)) * Running a WLS regression of y1 on x1, using w as weights ols y1 x1 / weight = w stop