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New Wine in Old Bottles: A Sequential Estimation Technique for the LPM

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  • Horrace, William C.

    (affiliation not available)

  • Oaxaca, Ronald L.

    (University of Arizona)

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.

Suggested Citation

  • Horrace, William C. & Oaxaca, Ronald L., 2003. "New Wine in Old Bottles: A Sequential Estimation Technique for the LPM," IZA Discussion Papers 703, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp703
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    References listed on IDEAS

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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Another Gripe About the Linear Probability Model
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-06-01 21:11:00

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    Cited by:

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    More about this item

    Keywords

    linear probability model; sequential least squares; consistency; Monte Carlo;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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