Publisher's Synopsis
The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topic 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. This text provides a modern, 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 the book offers a broad perspective, bringing together diverse material. Its comparative approach, emphasis on regression and prediction and numerous exercises and references provide a foundation for subsequent courses in econometrics and should 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 Challenger space 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. Appendices contain a review of matrix algebra, computation and statistical tables.
