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dc.contributor.advisorChairperson, Graduate Committee: Jeffrey T. LaFrance.en
dc.contributor.authorSiebrasse, Paul Benjaminen
dc.date.accessioned2013-06-25T18:38:03Z
dc.date.available2013-06-25T18:38:03Z
dc.date.issued1987en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/2277en
dc.description.abstractThe purpose of this study is to compare the small sample properties of a nonstandard estimator for first order autocorrelated errors in a time series equation with those of the more widely used estimators by using Monte Carlo experiments. The estimation method of interest arises either from the assumption that the presample residuals are not generated from an autoregressive process or from fixing the estimates of the presample values of the residuals at their unconditional expectations. This method has several nice properties. First, the estimator that is obtained is asymptotically equivalent to the standard methods. Second, the initial observations in the sample are retained, which overcomes problems that can arise in small samples when the independent variables are trended. Third, the data transformation that is used to estimate the unknown parameters of the model can be generalized to any order autoregressive process without any substantial increase in complexity. The results indicate that this nonstandard estimator performs very well relative to the other estimators considered for most experimental designs. This implies that the costs of using this more convenient estimation technique in terms of accuracy of parameter estimates is low relative to the other techniques considered.en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Agricultureen
dc.subject.lcshAutocorrelation (Statistics)en
dc.subject.lcshTime-series analysisen
dc.subject.lcshMonte Carlo methoden
dc.titleThe small sample properties of a nonstandard estimator in the context of first order autocorrelationen
dc.typeThesisen
dc.rights.holderCopyright 1987 by Paul Benjamin Siebrasseen
thesis.catalog.ckey20694en
thesis.degree.committeemembersMembers, Graduate Committee: Gail Crameren
thesis.degree.departmentAgricultural Economics & Economics.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage133en
mus.relation.departmentAgricultural Economics & Economics.en_US


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