By George G. Judge

ISBN-10: 0521689732

ISBN-13: 9780521689731

ISBN-10: 0521869595

ISBN-13: 9780521869591

This publication is meant to supply the reader with a company conceptual and empirical knowing of uncomplicated information-theoretic econometric types and strategies. simply because such a lot facts are observational, practitioners paintings with oblique noisy observations and ill-posed econometric versions within the kind of stochastic inverse difficulties. for this reason, conventional econometric equipment in lots of situations usually are not acceptable for answering some of the quantitative questions that analysts desire to ask. After preliminary chapters care for parametric and semiparametric linear likelihood types, the point of interest turns to fixing nonparametric stochastic inverse difficulties. In succeeding chapters, a relations of energy divergence measure-likelihood features are brought for a number conventional and nontraditional econometric-model difficulties. ultimately, inside both an empirical greatest chance or loss context, Ron C. Mittelhammer and George G. pass judgement on recommend a foundation for selecting a member of the divergence family members

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**Extra info for An Information Theoretic Approach to Econometrics**

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Note the following scaled deviation between the estimator and the unknown parameter vector β, as ˆ − β) = n1/2 (x x)−1 x ε = (n−1 x x)−1 n−1/2 x ε. 12) n1/2 (β The asymptotic normality of the LS estimator follows if n–1 x x → , where is a finite positive definite symmetric matrix, the elements in x are bounded in absolute value, and E[|εi |2 + δ ]≤ ξ, for some choice of positive finite constants δ and ξ. It follows via central limit theory that n−1/2 x ε = d n−1/2 ni=1 xi εi → N(0, ), where xi denotes the ith row of x written as a d column vector, in which case it follows that n1/2 (βˆ − β) → N(0, σ 2 −1 ).

And G. Casella (1998), Theory of Point Estimation. New York: SpringerVerlag. McCullagh, P. and J. A. Nelder (1989), Generalized Linear Models, 2nd ed. London: Chapman and Hall. Mittelhammer, R. C. (1996), Mathematical Statistics for Economics and Business. New York: Springer-Verlag. , G. Judge, and D. Miller (2000), Econometric Foundations. New York: Cambridge University Press. Newey, W. K. and D. McFadden (1994), “Large Sample Estimation and Hypothesis Testing,” in Handbook of Econometrics, edited by Robert F.

1985), Advanced Econometrics. Cambridge, MA: Harvard University Press, chapter 4. Bunke, H. and O. Bunke (1986), Statistical Inference in Linear Models. New York: Wiley. Huber, P. J. (1981), Robust Statistics. New York: John Wiley and Sons. Lehmann, E. and G. Casella (1998), Theory of Point Estimation. New York: SpringerVerlag. McCullagh, P. and J. A. Nelder (1989), Generalized Linear Models, 2nd ed. London: Chapman and Hall. Mittelhammer, R. C. (1996), Mathematical Statistics for Economics and Business.

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