Introduction to Bayesian Inference in Econometrics

Author: Arnold Zellner / Zhang Yaoting / Jiang Chuanhai / Shen Genxiang (Eds.)
Publisher:
Publishing Date: 2005-07-01
Features: This book is a classic work that systematically applies Bayesian inference to the field of econometrics and model building, as well as data analysis. Since its first publication in 1971, the book has been reprinted repeatedly and continues to have a lasting influence worldwide. The content on Bayesian inference and methods in econometrics introduced in the book remains relevant for statisticians and econometricians, as well as students in these fields. The book is comprehensive and systematic, consisting of twelve chapters and three appendices.
Chapter 1 lays the foundation for guiding readers into some basic themes of scientific philosophy and methodology. Chapter 2 lists and discusses the fundamental concepts and operations used in Bayesian methods for inference. Chapters 3 to 9 focus on several models commonly encountered in econometric research, conducting Bayesian analysis on them and comparing Bayesian conclusions with sampling theory. Chapter 10 addresses hypothesis testing and the comparison of hypotheses. Chapter 11 analyzes several control problems related to regression and other processes. In Chapter 12, the authors present some concluding remarks. At the end of each chapter, relevant problems are listed to help readers deepen their understanding and review the content after completing each chapter.
The three appendices at the end of the book introduce the properties of many important univariate and multivariate distributions and briefly describe how to apply computer programs for univariate and bivariate numerical integration. The book is written in a fluent and clear manner, using concise explanations and detailed mathematical derivations to articulate the complex concept of Bayesian inference in econometrics. It is an excellent resource that can benefit professionals in statistics and econometrics, as well as advanced undergraduate and graduate students.