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 types and strategies. simply because such a lot info are observational, practitioners paintings with oblique noisy observations and ill-posed econometric types within the type of stochastic inverse difficulties. hence, conventional econometric equipment in lots of situations aren't appropriate for answering a number of the quantitative questions that analysts desire to ask. After preliminary chapters care for parametric and semiparametric linear chance versions, the focal point turns to fixing nonparametric stochastic inverse difficulties. In succeeding chapters, a family members of energy divergence measure-likelihood features are brought for a number of conventional and nontraditional econometric-model difficulties. eventually, 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 relations
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Additional resources for An Information Theoretic Approach to Econometrics
2 Asymptotic Properties of E Estimators E estimators are both consistent and asymptotically normally distributed under general regularity conditions. We provide some brief intuition here for why it is reasonable to expect that these properties are achieved quite generally for E estimators when the estimation objective function is chosen appropriately. We also note that in general, E estimators can be cast in terms of estimating equations; thus, properties relating to the latter estimator, which are discussed in Chapter 3, apply as well to E estimators.
3) cannot be calculated, and so minimizing E [s(β, Y, x)] is not an operational objective. 2). 3). 2) through choice of β should result in an estimate close to β0 for large enough n. 18 Formulation and Analysis of Parametric and Semiparametric Linear Models The concept of minimizing a random variable, s(β, Y, x), through the choice of β, is not a standard minimization problem. However, we can minimize the outcome s(β, y, x) for every possible contingency for the outcome y of Y, which is a standard minimization problem.
1998), Asymptotic Statistics, Cambridge: Cambridge University Press. White, H. (1984), Asymptotic Theory for Econometricians. Orlando, FL: Academic Press. 1 Introduction In the previous chapter, we generalized the linear model to include stochastic regressors and examined the corresponding statistical implications. In this chapter, we examine estimation and inference methods for obtaining information on parameters that are functionally related to moments of the sampling distribution underlying a sample of data.