By George G. Judge
This e-book is meant to supply the reader with a company conceptual and empirical knowing of simple information-theoretic econometric versions and techniques. simply because such a lot information are observational, practitioners paintings with oblique noisy observations and ill-posed econometric versions within the type of stochastic inverse difficulties. therefore, conventional econometric equipment in lots of situations aren't acceptable for answering a number of the quantitative questions that analysts desire to ask. After preliminary chapters take care of parametric and semiparametric linear likelihood types, the point of interest turns to fixing nonparametric stochastic inverse difficulties. In succeeding chapters, a relatives of energy divergence measure-likelihood features are brought for a number of conventional and nontraditional econometric-model difficulties. ultimately, inside both an empirical greatest probability or loss context, Ron C. Mittelhammer and George G. pass judgement on recommend a foundation for selecting a member of the divergence relations
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Additional resources for An Information Theoretic Approach to Econometrics
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. New York: Springer-Verlag. , G. Judge, and D. Miller (2000), Econometric Foundations. New York: Cambridge University Press. Newey, W. K. and D.
1 The Squared Error Metric and the Least Squares (LS) Principle The least squares (LS) principle for providing a solution to the semiparametric general linear model dates back to Gauss, Laplace, and Legendre in the early nineteenth century. 2) with respect to the choice of β. One supporting argument for use of the metric is based on the following simple reasoning. Letting β0 denote the true value of the parameter vector, the linear semiparametric model characteristics imply that E [s(β, Y, x)] = n−1 E [(Y − xβ0 + xβ0 − xβ) (Y − xβ0 + xβ0 − xβ)] = σ 2 + (β0 − β) (n−1 x x)(β0 − β).
2 Linear Model Semiparametric Estimation through Moment Equations In this section, we apply the MOM concept to the case of a general linear model using the moment-based condition that the noise component elements and the explanatory variable values are orthogonal (or, equivalently, contemporaneously uncorrelated).
An Information Theoretic Approach to Econometrics by George G. Judge