By George G. Judge
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
Read Online or Download An Information Theoretic Approach to Econometrics PDF
Best econometrics books
Very important new advancements have happened that experience major influence at the evolution of econometrics, specifically, the tip of the chilly struggle and the emergence of the data revolution in approximately all economies of the realm. the data revolution has had major influence on information flows, making them even more well timed, obtainable, and descriptive of extra elements of the economic climate.
This paperback variation is a reprint of the 1991 version. Time sequence: idea and strategies is a scientific account of linear time sequence types and their program to the modeling and prediction of information accrued sequentially in time. the purpose is to supply particular strategies for dealing with information and while to supply a radical knowing of the mathematical foundation for the ideas.
This publication presents a vast, mature, and systematic advent to present monetary econometric versions and their purposes to modeling and prediction of monetary time sequence info. It makes use of real-world examples and genuine monetary information through the e-book to use the versions and strategies defined.
This e-book offers a quantitative framework for the research of clash dynamics and for estimating the commercial expenses linked to civil wars. the writer develops converted Lotka-Volterra equations to version clash dynamics, to yield practical representations of conflict approaches, and to permit us to evaluate lengthy clash traps.
- Mathematics for Econometrics
- An Introduction to Classical Econometric Theory
- Analysis of Microdata
- Dynamic Model Analysis: Advanced Matrix Methods and Unit-Root Econometrics Representation Theorems, Second Edition
Extra info for An Information Theoretic Approach to Econometrics
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.
An Information Theoretic Approach to Econometrics by George G. Judge