* Specification Tests on OLS Regression for Hourly Wages * * Keywords: * regression, log, ols, specification, test, f-test, omitted, variable, * heteroskedasticity, white, errors, jarque-berra, hourly, wage * * Description: * We illustrate how to estimate Log-Linear and Polynomial OLS Models for * Hourly Wages and apply Specification Tests on them, including Omitted * Variable, Heteroskedasticity and Jarque-Berra Normality Tests, and create * histograms and graphs of residuals * * Author(s): * Skif Pankov * * Source: * Damodar N. Gujarati and Dawn C. Porter, Basic Econometrics - 5th Edition * McGraw-Hill International Edition, Chapter 13, Example 13.11-1 (page 500) * sample 1 1289 * Reading the datafile and naming variables read(data_13.11-1.shd) wage fe nw un edu exp wk * Generating the log of wages, interraction dummy variables and a square of * experience genr lnwage = log(wage) genr fenw = fe*nw genr feun = fe*un genr fewk = fe*wk genr exp2 = exp**2 * Running an OLS regression of lnwage on original variables, stating to * include residual statistics ols lnwage edu exp fe nw un wk / rstat * Running a modified version of the previous OLS regression by including * interraction dummy variables - regression is run in order to test the * joint significance of the inluded dummy variables ols lnwage edu exp fe nw un wk fenw feun fewk * Conducting an F-test of the joint significance of interraction dummies test test fenw = 0 test feun = 0 test fewk = 0 end * Including the square of the exp variable into the first regression * to test the linearity of the exp ols lnwage edu exp fe nw un wk exp2 / rstat * Testing the previous OLS regression for heteroskedasticity diagnos / het * Running the previous OLS regression with White's heteroskedasticity * corrected standard errors, saving residuals, predicted values and testing * for normality of residuals using Jarque-Bera test ols lnwage edu exp fe nw un wk exp2 / rstat hetcov resid = reds predict = elnwage gf * Creating a histogram of residuals from previous regression, stating to * use 30 histogram groups graph reds / histo range groups = 30 * Creating a graph of reds against elnwage graph reds elnwage * Showing descriptive statistics of variables wage, edu and exp stat wage edu exp / all * Testing wage, edu and exp for normality using Jarque-Bera test ols wage / gf ols edu / gf ols exp / gf stop