Mô tả
Thông tin chi tiết về Introduction to Bayesian Econometrics
SKU | 2107644992130 |
• Emphasis on classical and Markov chain Monte Carlo (MCMC) methods of simulation, including the derivation of algorithms
• Applications to a wide variety of standard econometric models used in economics, political science, biostatistics and other applied fields
• Presents the key topic of subjective probability, and provides a more extensive treatment of prior distributions than the competing texts
This concise textbook is an introduction to econometrics at the graduate or advanced undergraduate level. It differs from other books in econometrics in its use of the Bayesian approach to statistics. This approach, in contrast to the frequentist approach to statistics, makes explicit use of prior information and is based on the subjective view of probability, which takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters.
Edward Greenberg is Professor Emeritus of Economics at Washington University in St. Louis, where he served as a full professor on the faculty from 1969 to 2005. Professor Greenberg also taught at the University of Wisconsin, Madison and has been a visiting professor at the University of Warwick (UK), Technion University (Israel), and the University of Bergamo (Italy). A former holder of a Ford Foundation Faculty Fellowship, Professor Greenberg is coauthor of four books: Wages, Regime Switching, and Cycles (1992), The Labor Market and Business Cycle Theories (1989), Advanced Econometrics (1991), and Regulation, Market Prices, and Process Innovation (1979). His published research has appeared in leading journals such as the American Economic Review, Econometrica, Journal of Econometrics, Journal of the American Statistical Association, Biometrika, and the Journal of Economic Behavior and Organization. Professor Greenberg’s current research intersts include dynamic macroeconomics as well as Bayesian econometrics.
“This book provides an excellent introduction to Bayesian econometrics and statistics with many references to the recent literature that will be very helpful for students and others who have a good background in the calculus. Basic Bayesian estimation, testing, prediction and decision techniques are clearly explained with applications to a broad range of models and many computed examples are provided to illustrate general principles. Classical and modern computing techniques are clearly explained and applied to solve central inference problems. Also, references to downloadable computer algorithms are included in this impressive book.”
— Arnold Zellner, Graduate School of Business, University of Chicago
Part I. Fundamentals of Bayesian Inference:
1. Introduction
2. Basic concepts of probability and inference
3. Posterior distributions and inference
4. Prior distributions
Part II. Simulation: 5. Classical simulation
6. Basics of Markov chains
7. Simulation by MCMC methods
Part III. Applications:
8. Linear regression and extensions
9. Multivariate responses
10. Time series
11. Endogenous covariates and sample selection
Appendix A: Probability distributions and matrix theorems
Appendix B: Computer programs for MCMC calculations.
Đánh giá
Clear filtersChưa có đánh giá nào.