By Thomas B. Fomby, R. Carter Hill, Stanley R. Johnson
This e-book had its perception in 1975in a pleasant tavern close to the varsity of Businessand PublicAdministration on the UniversityofMissouri-Columbia. of the authors (Fomby and Hill) have been graduate scholars of the 3rd (Johnson), and have been (and are) keen on educating econometrics successfully on the graduate point. We made up our minds then to write down a publication to function a complete textual content for graduate econometrics. often, the fabric incorporated within the bookand itsorganization were ruled by means of the query, " Howcould the topic be most sensible provided in a graduate class?" For content material, this has intended that we've got attempted to hide " all of the bases " and but haven't tried to be encyclopedic. The meant function has additionally affected the levelofmathematical rigor. we have now tended to turn out in simple terms these effects which are easy and/or fairly common. Proofs that will call for inordinant quantities of sophistication time have easily been referenced. The e-book is meant for a two-semester direction and paced to confess extra broad remedy of components of particular curiosity to the trainer and scholars. now we have nice self belief within the skill, undefined, and patience of graduate scholars in ferreting out and knowing the passed over proofs and effects. finally, this is often how one earnings adulthood and a fuller appreciation for the topic at the least. it really is assumed that the readers of the publication could have had an econometric equipment direction, utilizing texts like J. Johnston's Econometric tools, 2d ed.
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Additional info for Advanced Econometric Methods
Ramsey, J. B. (1969). Tests for specification errors in classical linear least squares regression analysis. Journal of Royal Statistical Society, B, 31, 350-371. Rao, C. R. (1973). Linear Statistical Inference and Its Applications , 2nd ed. New York : Wiley. Schmidt , P. (1976). Econometrics . New York : Marcel Dekker. Theil, H. (1971). Principles of Econometrics. New York : Wiley. Wilks, S. S. (1962). Mathematical Statistics. New York : Wiley. 1 Introduction The ordinary least squares and generalized least squares results reviewed in Chapter 2 dealt with the efficiency of estimation methods in the classical linear regression model and the generalized least squares model.
Small sample efficiencyis to be contrasted with largesampleefficiency studied in the next chapter where desirable properties of estimators are attained only as the sample size T becomes arbitrarily large. There are two distinct methods for determining if an unbiased estimator possesses the minimum variance unbiased property ; use of the Cramer-Rae lower bound or the statistical properties of completeness and sufficiency. The method of primary interest here is the Cramer-Rae lower bound as it willplaya prominent role in our discussion of asymptotic sampling properties in the next chapter.
The stringent linearity assumption is, in a sense, compensation for the lack of knowledge concerning the specific form of the distribution of the error term. In the presence of more explicit information, the linearity requirement can be dropped. Thanks to work by such mathematical statisticians as Cramer, Rao, Blackwell, Fisher, Neyman, Lehmann, and Scheffe, small sample efficiency among unbiased estimators can be straightforwardly determined in the presence of information concerning the parent distribution of the random variable(s) under investigation.