This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
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 15.115.7) 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/outofthelaborforce). 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)....
View
Full Document
 Spring '06
 IMBENS
 Normal Distribution, Maximum likelihood, Likelihood function, Yi

Click to edit the document details