lecture demo 4-9-15 - This demo will illustrate methods for modeling binary dependent variables They will be applied to the DOE survey of high school

# lecture demo 4-9-15 - This demo will illustrate methods for...

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* This demo will illustrate methods for modeling binary dependent variables. * They will be applied to the DOE survey of high school students that we used for * PS4 (with west and non-west portions combined). After reading the data, we plot * the variable "somecollege" (i.e., more than 12 years of education) against "tuition." use "collegeall.dta" drop if tuition==. twoway (scatter somecollege tuition) * First estimate a Linear Probability Model (LPM) of college attendance on * tuition. The slope coefficient gives us the marginal effect of tuition on * attendance. Thinking of this relation as demand for education function, we * can compute the tuition elasticity of the demand for college: the percentage * change in the PROBABILITY of attending college associated with a 1% increase * in tuition cost. Below this is done 'manually' and then again using Stata's * margins command. The answer is the same in both. Note that this is the * average of tuition elasticities of the sample, as opposed to the tuition * elasticity at the average tuition. regress somecollege tuition , robust predict regprob , xb gen regelas = _b[tuition]*tuition/regprob summ regelas margins , eyex(tuition)

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