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Systems of regression equations

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dc.contributor.advisor Markham R, Prof en
dc.contributor.author Carstens SC en
dc.date.accessioned 2016-09-22T11:54:27Z
dc.date.available 2016-09-22T11:54:27Z
dc.date.submitted 1990 en
dc.identifier.uri http://hdl.handle.net/20.500.11892/141929
dc.description.abstract Estimation of the parameters in a system of regression equations are usually being calculated by ordinary least squares, generalized least squares or maximum likelihood estimation. Ordinary least squares estimation is used on the equations separately and produces best, linear and unbiased estimates only if the regression equations are not correlated via the disturbance terms or if the disturbances are not generated by an autoregressive moving average process. <br><br> Generalized least squares and maximum likelihood estimation are applied to the regression equations together and provide best, linear and unbiased estimates. If, however, the covariance matrix of the disturbances is unknown, these two estimation procedures result in numerical optimization procedures. <br><br> In this study it is shown that the Kalman filter, in addition to the state .space approach to a system of equations, is a superior estimation method when correlation among the disturbances of different equations exists. en
dc.language English en
dc.subject Mathematics, mathematical statistics and statistics en
dc.subject Statistics (General) en
dc.title Systems of regression equations en
dc.type Masters degree en
dc.description.degree MSc en


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