New Wine in Old Bottles: A Sequential Estimation Technique for the LPM
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, perhaps, justified for practical reasons. A sequential least squares (SLS) esti-mation 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. An empirical example is provided.
|Date of creation:||19 Jun 2002|
|Date of revision:||11 May 2003|
|Note:||Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP; pages: 43; figures: included. A new estimation technique for the LPM model|
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