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By Peter Kennedy

6th variation and the final variation there'll ever be, because the writer handed away.


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Additional resources for A Guide to Econometrics (6th Edition)

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A reasonable correc­ tion is to forecast using exp { llly - Y2 V(fe)} where V(fe) is an estimate of the variance of the forecast error. See Kennedy (1983). The formula for this variance can be found in example (d) of section 5 of appendix B; its magnitude depends on the regressor values associafed with the value to be forecast. Estimation of this variance can most easily be done by using an observation-specific dummy as described in chapter 15.

The MLE is the particular pair of values µMLE and (a2)MLE that creates the greatest probability of having obtained the sample in question; that is, no other pair of values would be preferred to this maximum likelihood pair, in the sense that pair B is preferred to pair A. The means by which the econometrician finds this MLE is discussed in the technical notes to this section. In addition to its intuitive appeal, the maximum likelihood estimator has several desirable asymptotic properties. It is asymptotically unbiased, it is consistent, it is asymptotically efficient, it is distributed asymptotically normally, and its asymptotic variance can be found via a standard formula (the Cramer-Rao lower bound - see the technical notes to this section).

Students are expected to recognize that some approximate model with parameter values 8. formulas are special cases of more general Find the /3* values that cause the simulated formulas. data to produce 8 estimates that are closest 5. Discussions of variance, and appropriate for­ to the 8 estimates obtained using the actual mulas, are seldom gathered together in one data. A more detailed discussion appears in place for easy reference. chapter 23. (g) Nearness/concentration Some estimators Appendix B has been included at the end of have infinite variances and for that reason are this book to alleviate this confusion, supplement­ often dismissed.

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