By George G. Judge
This e-book is meant to supply the reader with an organization conceptual and empirical realizing 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 types within the kind of stochastic inverse difficulties. hence, conventional econometric tools in lots of situations are usually not acceptable for answering the various quantitative questions that analysts desire to ask. After preliminary chapters take care of parametric and semiparametric linear chance types, the focal point turns to fixing nonparametric stochastic inverse difficulties. In succeeding chapters, a kinfolk of energy divergence measure-likelihood features are brought for more than a few conventional and nontraditional econometric-model difficulties. ultimately, inside of 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 kin. [C:\Users\Microsoft\Documents\Calibre Library]
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Extra info for An Information Theoretic Approach to Econometrics Paperback
However, we can use the sample data y and define sample moments m′1 = n−1 i y i = y¯ n and m′2 = n−1 i y 2i as estimates of the respective population moments (μ′1 , μ′2 ), and obtain estimates of the parameters via substitution as ⎡ ⎤ y¯ n ′ ˆ m1 β ⎦.
In this case, the distribution of Y|x is in fact determined by a mean-shifting of the distribution of ε; that is, (Y|x) ∼ xβ + ε. Thus, once the distribution of ε is specified, the distribution of Y|x is defined directly via mean translation of the distribution of ε. 1 if ε ∼ N(0, σ 2 I), because then (Y|x) ∼ N(xβ, σ 2 I). 12) where ξ denotes the parameters of the marginal probability distribution of X, g (x; ξ). 12) characterize the situation where X is weakly exogenous to the DSP generating the outcomes of Y.
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. McFadden (1994), “Large Sample Estimation and Hypothesis Testing,” in Handbook of Econometrics, edited by Robert F.