Results on the bias and inconsistency of ordinary least squares for the linear probability model
By W. Horrace, R. Oaxaca. 2006
542 citations recorded by the source corpus.
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear probability model and provides sufficient conditions for unbiasedness and consistency to hold. The conditions suggest that a btrimming estimatorQ may reduce OLS bias. D 2005 Elsevier B.V. All rights reserved.
Subfields: Environmental Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d31495f0b85c06cd6f9e7671974be40df168a6e4
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In Environmental Engineering
In Statistics and Probability
Citations in the corpus
- Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey (2020, unknown): “For further information on limitations and potential biases introduced from estimating a probability model using ordinary least squares (OLS) see Horrace and Oaxaca (2006).”
- Privacy Regulation and Online Advertising (2010, background): “This means that the potential bias of the linear probability model if predicted values lie outside of the range of zero and one (Horrace and Oaxaca 2006) is not an issue in our estimation.”
- Crowdfunding: Geography, Social Networks, and the Timing of Investment Decisions (2015, unknown): “Therefore the potential bias of the linear probability model is reduced in our estimation (Horrace and Oaxaca 2006).”
- Responses to Entry in Multi-Sided Markets: The Impact of Craigslist on Local Newspapers (2014, result): “As a result, the linear probability models with robustness standard errors yield unbiased and consistent estimates in our case (Horrace and Oaxaca 2006).”
- Online Display Advertising: Targeting and Obtrusiveness (2011, methodology): “…about using the linear probability model, which is the potential for biased estimates and predicted probabilities outside the range of 0 and 1 (see Horrace and Oaxaca 2006 for a discussion), is less likely in our setting because the mass point of dependent variable is far from 0 or 1 and because…”