Ron Mittelhammer (Prof.), George G. Judge, Douglas J. Miller
Description
The focus of this book is to develop a plausible basis for reasoning in situations involving incomplete-partial information and developing principles
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and procedures for learning or recovering information from a sample of economic data. The book starts with the specification and analysis of the simplest parametric and semiparametric probability models. The following chapters then generalize the specification and the reasoning process. The authors' objective is to cover a sufficient scope of concepts and procedures to give the reader an adequate conceptual foundation from which to choose, learn about, and implement methods of analysis that avoid assumptions one does not wish to make in the formulation, estimation, and inference of probability models consistent with economic sampling processes.\par The book is organized into ten parts. Part I discusses a general approach to searching for econometric knowledge and introduces an array of fundamental probability-econometric models that are used in practice to characterize economic data sampling processes. Part II is concerned with estimation and inference procedures for parametric and semiparametric variants of the linear regression models. The concept of extremum estimators is introduced in Part III; additionally, nonlinear-in-the-parameters regression models and nonnormal errors are considered. Part IV is concerned with stochastic right-hand-side variables, moment based specifications of the data sampling processes, empirical likelihood, and information theoretic procedures whose solutions cannot be written in closed form. In Part V, the restrictive i.i.d. noise component structure of the probability model is relaxed, and estimation and inference procedures for handling this more general error covariance model are developed.\par In Part VI instrumental variables, the general method of moments for overdetermined problems, and the simultaneous equations probability model are examined. Model selection problems are considered in Part VII. Information recovery in discrete choice and nonparametric models is discussed in Part VIII. Part IX deals with basic concepts of Bayesian inference and their applications to regression models in the face of different posterior distributions. Part X ends the book with an assessment of the econometric developments in this book as well as with an assessment of possible econometric challenges.\par The book contains a CD with various statistical programs, written in GAUSS, which can be used to replicate the procedures developed in this book.
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