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ANALYSIS OF BINARY DEPENDENT VARIABLES USING LINEAR PROBABILITY MODEL AND LOGISTIC REGRESSION: A REPLICATION STUDY
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

Linear Probability Model (LPM) is commonly used because it is easy to compute and interpret than with logits and probits even though the estimated probabilities may fall outside the $\big[$0,1$\big]$ interval and the linearity concept does not make much sense when dealing with probabilities. This paper extends upon the results of \citeA{Dara} reviewing the use of LPM to examine if alcohol prohibition reduces domestic violence. Regular LPM resulted in inconclusive estimates since prohibition was omitted due to collinearity as controls were added. However \citeA{Dara} had results, and further inspection on their regression commands showed that they ran a linear regression, then a post-estimation on residuals and further used residuals as a dependent variable hence the results were different from the regular LPM. Their method still resulted in unbounded predicted probabilities and heteroscedastic residuals, thus showing that OLS was inefficient and a non-linear binary choice model like logistic regression would be a better option. Logistic regression predicts the probability of an outcome that can only have two values and was therefore used in this paper. Unlike LPM, logistic regression uses a non-linear function which results in a sigmoid bounding the predicted outcome between 0 and 1. Logistic regression had no complication; thus logistic (or any another non-linear dichotomous dependent variable models) regression should have been used on the final analysis while LPM is used at a preliminary stage to get quick results.

Place, publisher, year, edition, pages
2019. , p. 30
Keywords [en]
binary choice models, logistic regression, linear probability model, forbidden regression, binary dependent variables, dichotomous variables, residuals as dependent variables
National Category
Economics Other Natural Sciences
Identifiers
URN: urn:nbn:se:uu:diva-385535OAI: oai:DiVA.org:uu-385535DiVA, id: diva2:1324943
Educational program
Master Programme in Statistics
Presentation
2019-06-04, B105, Kyrkogårdsgatan 10, Uppsala, 11:05 (English)
Supervisors
Examiners
Available from: 2019-06-18 Created: 2019-06-14 Last updated: 2019-06-18Bibliographically approved

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