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Unformatted text preview: Imbens, Lecture Notes 11, ARE213 Spring ’06 1 ARE213 Econometrics Spring 2006 UC Berkeley Department of Agricultural and Resource Economics Discrete Response Models I: Binary Response Models (W 15.1-15.7) In the next couple of lectures we consider models where the dependent variable is dis- crete. Initially, we look at the case where the outcome is binary: yes/no, participation/no- participation, employed/unemployed. After that we will look at more complicated cases where the outcome may take on a number of values, possibly ordered (highschool dropout / highschool /college), or categorical (employed/unemployed/out-of-the-labor-force). The example I will use in this lecture is the decision to go to college. I will use the white subsample of the NLS data, 815 observations. Out of these 419 go on to college, and the remainder left school before or after getting a high school degree. We will model this as a function of three covariates, iq, father’s education and mother’s education. The simplest thing to do is to use a linear probability model: Y i = X i β + ε, with the minimal assumption on ε i being that it is uncorrelated with X i . Obviously the model has to have heteroskedasticity: if the conditional expectation is E [ Y | X ] = Pr( Y = 1 | X ) = X β , it must be that V ( Y | X ) = X β (1- X β ). That is not to big a problem in)....
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- Spring '06
- Normal Distribution, Maximum likelihood, Likelihood function, Yi