New Wine in Old Bottles: A Sequential Estimation Technique for the LPM
AbstractThe 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.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 703.
Length: 34 pages
Date of creation: Jan 2003
Date of revision:
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Other versions of this item:
- William C. Horrace & Ronald L. Oaxaca, 2002. "New Wine in Old Bottles: A Sequential Estimation Technique for the LPM," Econometrics 0206002, EconWPA, revised 11 May 2003.
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-02-10 (All new papers)
- NEP-DCM-2003-02-10 (Discrete Choice Models)
- NEP-ECM-2003-02-15 (Econometrics)
- NEP-PKE-2003-02-10 (Post Keynesian Economics)
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