Intermediate Economic Statistics

The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses-giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics. Poirier's text provides a thoroughly modern, self-contained, comprehensive, and accessible treatment of the probability and statistical foundations of econometrics with special emphasis on the linear regression model. Written primarily for advanced undergraduate and graduate students who are pursuing research careers in economics, Intermediate Statistics and Econometrics offers a broad perspective, bringing together a great deal of diverse material. Its comparative approach, emphasis on regression and prediction, and numerous exercises and references provide a solid foundation for subsequent courses in econometrics and will prove a valuable resource to many nonspecialists who want to update their quantitative skills. The introduction closes with an example of a real-world data set-the Challengerspace shuttle disaster-that motivates much of the text's theoretical discussion. The ten chapters that follow cover basic concepts, special distributions, distributions of functions of random variables, sampling theory, estimation, hypothesis testing, prediction, and the linear regression model. Appendixes contain a review of matrix algebra, computation, and statistical tables.

V.1: An integration of economic theory and statistical methods. v.2. A guide to recent developments and literature 1968-1978.

This book covers the typical material of an intermediate macroeconomics course at the undergraduate level. The approach is both theoretical and statistical, with the theory being limited to algebraic expressions and the statistics to simple and multiple regression and correlation. The coverage is traditional for the course (being IS-LM in its focus), and the tests are of the consumption function, investment function, demand for money, Phillips curve, etc. Every effort is made to explain the statistics, with some explicit statistical material embedded in the text and several ?how to? sections in the Appendix geared to the popular programs Eviews and Excel. There is also a set of Internet links that instructors can readily access in order to supplement and update the data and to use to provide the data for the students to work the exercises.The book is intended as a text for an intermediate economics course and has been used as such at North Carolina State University. There are full sets of review questions, discussion questions, problems, and computer exercises attached to each chapter, all of which have been classroom-tested. In addition to undergraduates (especially advanced undergraduates), graduate instructors will benefit from the book; and both the professional and the graduate student will find the explanations and applications useful in their work.

This book explains how to use R software to teach econometrics by providing interesting examples, using actual data applied to important policy issues. It helps readers choose the best method from a wide array of tools and packages available. The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression. The R program snippets are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification.

Combines technique with application using real data sets. The core of the book (Chapters 1-13) covers the basic statistical concepts necessary for econometrics with an emphasis on regression analysis. Part V is a treatment of advanced econometrics theory.