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1. Linear Probability Model vs. Logit (or Probit)

EEP/IAS 118 Andrew Dustan Section Handout 13 1. Linear Probability Model vs. Logit (or Probit) We have often used binary ("dummy") variables as explanatory variables in regressions. What about when we want to use binary variables as the dependent variable? It's possible to use OLS: = + + + + where y is the dummy variable. This is called the Linear Probability Model . Estimating the equation: = 1| = = + + + is the predicted Probability of having = 1 for the given values of .. Problems with the Linear Probability Model (LPM): 1. Heteroskedasticity: can be fixed by using the "robust" option in Stata. Not a big deal. 2. Possible to get < 0 or > 1.

Linear Probability Model Logit (probit looks similar) This is the main feature of a logit/probit that distinguishes it from the LPM – predicted probability of =1 is never below 0 or above 1, and the shape is always like the one on the right rather than a straight line.

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