Least Squares Estimation for the Gauss–Markov Model

Dale L. Zimmerman
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Dale L. Zimmerman: University of Iowa, Department of Statistics and Actuarial Science

Chapter 7 in Linear Model Theory, 2020, pp 115-148 from Springer

Abstract: Abstract Now that we have determined which functions c Tβ in a linear model {y, Xβ} can be estimated unbiasedly, we can consider how we might actually estimate them. This chapter presents the estimation method known as least squares. Least squares estimation involves finding a value of β that minimizes the distance between y and Xβ, as measured by the squared length of the vector y −Xβ. Although this seems like it should lead to reasonable estimators of the elements of Xβ, it is not obvious that it will lead to estimators of all estimable functions c Tβ that are optimal in any sense. It is shown in this chapter that the least squares estimator of any estimable function c Tβ associated with the model {y, Xβ} is linear and unbiased under that model, and that it is, in fact, the “best” (minimum variance) linear unbiased estimator of c Tβ under the Gauss–Markov model {y, Xβ, σ 2I}. It is also shown that if the mean structure of the model is reparameterized, the least squares estimators of estimable functions are materially unaffected.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-52063-2_7

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DOI: 10.1007/978-3-030-52063-2_7

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