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dc.creatorGonen, Mithat
dc.date.available2011-02-18T19:35:29Z
dc.date.issued1996-08
dc.identifier.urihttp://hdl.handle.net/2346/11306en_US
dc.description.abstractThis study is about the asymptotic distribution of the least squares estimator in nonstationary first-order autoregressive processes. These processes are commonly used to model economic time series and the desired distribution is important in finding the size of the so-called unit root tests. Our approach is based on the asymptotic characterization of the distribution in terms of a functional of the standard Wiener process. We use the Karhunen- Loeve expansion for the Wiener process and obtain the solution using characteristic functions and the Fourier inversion theorem. As compared to the previous studies, our method provides a conceptually simple framework in which one can investigate more complicated models.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.subjectAutoregression (Statistics)en_US
dc.subjectLeast squaresen_US
dc.subjectAsymptotic distribution (Probability theory)en_US
dc.titleAsymptotic distribution of the least squares estimator in the first-order autoregressive process
dc.typeThesis
thesis.degree.nameM.S.
thesis.degree.levelMasters
thesis.degree.disciplineStatistics
thesis.degree.grantorTexas Tech University
thesis.degree.departmentStatistics
thesis.degree.departmentMathematics and Statistics
dc.degree.departmentStatisticsen_US
dc.rights.availabilityUnrestricted.


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