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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.

=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 . This makes no sense—you can't have a probability below 0 or above 1.

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