By Peter Kennedy
6th version and the final version there'll ever be, because the writer handed away.
The add is a experiment of the library reproduction, processed via ScanTailor and OCRed by way of Acrobat itself. Chapter-level bookmarks added.
The hide picture you spot is from the Amazon website; it's for the Wiley free up. The ebook I'm importing is Blackwell liberate and has a unique disguise.
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Additional resources for A Guide to Econometrics (6th Edition)
There is an additional reason for interest in asymptotic properties, however, of equal importance. Often the derivation of small-sample prop erties of an estimator is algebraically intractable, whereas derivation of large-sample properties is not. This is because, as explained in the technical notes, the expected value of a nonlinear function of a statistic is not the nonlinear function of the expected value of that statistic, whereas the plim of a nonlinear function of a statistic is equal to the nonlinear function of the plim of that statistic.
In many cases it happens that a biased estimator becomes less and less biased as the sample size becomes larger and larger - as the sample size becomes larger its sampling distribution changes, such that the mean of its sampling distribution shifts closer to the true value of the parameter being estimated. Econometricians have formalized their study of these phenomena by structuring the concept of an asymptotic distribution and defining desirable asymptotic or "large-sample properties" of an esti mator in terms of the character of its asymptotic distribution.
Would you prefer to obtain your estimate of f3 by making a single random draw out off (/3*) or out ofj (fi)? , choosing the estimator that minimizes this weighted average). This is not a viable formalization, however, because the bias could be negative. 4 MES trades off bias and variance. abso�ute value of the bias; a more popular way is to use its square. When the estimator is chosen so as to minimize a weighted average of the variance and the square of the bias, the estimator is said to be chosen on the weighted square error criterion.