A comparison study on the estimation in Tobit regression models

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dc.contributor.author Leiker, Antoinette
dc.date.accessioned 2012-05-07T16:58:29Z
dc.date.available 2012-05-07T16:58:29Z
dc.date.issued 2012-05-07
dc.identifier.uri http://hdl.handle.net/2097/13804
dc.description.abstract The goal of this report is to compare various estimation procedures on regression models in which the dependent variable has a restricted range. These models, called Tobit models, are seeing an increase in use among economists and market researchers, specifically. Only the standard Tobit regression model is discussed in the report. First we will examine the five estimation methods discussed in Amemiya (1984) for standard Tobit model. These methods include Probit maximum likelihood, least squares, Heckman’s two-step, Tobit maximum likelihood, and the EM algorithm. We will examine the algorithm utilized in each method’s estimation process. We will then conduct simulation studies using these estimation procedures. Twelve scenarios have been considered consisting of three different truncation threshold on the response variable, two distributions of covariates, and the error variance known and unknown. The results are reported and a discussion of the goodness of each method follows. The study shows that the best method for estimating Tobit regression models is indeed the Tobit maximum likelihood estimation. Heckman’s two-step method and the EM algorithm also estimate these models well when the truncation rate is low and the sample size is large. The simulation results show that the Least squares estimation procedure is far less efficient than other estimation procedures. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Tobit regression en_US
dc.title A comparison study on the estimation in Tobit regression models en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Weixing Song en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth August en_US


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