Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/20634 
Erscheinungsjahr: 
2003
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 703
Verlag: 
Institute for the Study of Labor (IZA), Bonn
Zusammenfassung: 
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.
Schlagwörter: 
linear probability model
sequential least squares
consistency
Monte Carlo
JEL: 
C25
Dokumentart: 
Working Paper

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