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A Guide to Econometrics (6th Edition) by Peter Kennedy

By Peter Kennedy

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


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Monte Carlo studies (see section 2. 1 0) have shown that in general this supposition is warranted. The message of the discussion above is that when estimators with attractive small­ sample properties cannot be found, one may wish to choose an estimator on the basis of its large-sample properties. 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.

The traditional formula SSE/(N - K), where N is the number of observations and K is the number of explanatory variables (including a constant), is under gen­ eral conditions the best quadratic unbiased esti­ mator of cl. When K does not include the constant (intercept) term, this formula is written as SSE ! (N - K 1). Although in many instances it is mathemati­ cally impossible to determine the best unbiased estimator (as opposed to the best linear unbi­ ased estimator), this is not the case if the spe­ cific distribution of the error is known.

Of common trends. Ames and Reiter ( 1 96 1 ) Now there is much more analysis using graphfound, for example, that on average the R2 of ics, nonparametrics, simulation, bootstrapping, a relationship between a randomly chosen variMonte Carlo, Bayesian statistics, and data exploable and its own value lagged one period is ration/mining, all discussed in later chapters. 5 could • Problems with the accuracy of computer calculabe obtained by selecting an economic time series tions are ignored in practice, but can be considerand regressing it against two to six other ranable, as discussed at length by McCullough and domly selected economic time series.

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