Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/20634
Authors: 
Horrace, William C.
Oaxaca, Ronald L.
Year of Publication: 
2003
Series/Report no.: 
IZA Discussion paper series 703
Abstract: 
The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or a good approximation to the probability generating process. A sequential least squares (SLS) estimation procedure is introduced that may outperform OLS in terms of finite sample bias and yields a consistent estimator. Monte Carlo simulations reveal that SLS outperforms OLS, probit and logit in terms of mean squared error of the predicted probabilities.
Subjects: 
linear probability model
sequential least squares
consistency
Monte Carlo
JEL: 
C25
Document Type: 
Working Paper

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