Introduction to Bayesian Econometrics
Cambridge University Press (Verlag)
978-0-521-85871-7 (ISBN)
- Titel erscheint in neuer Auflage
- Artikel merken
This book introduces the increasingly popular Bayesian approach to statistics to graduates and advanced undergraduates. In contrast to the long-standing frequentist approach to statistics, the Bayesian approach makes explicit use of prior information and is based on the subjective view of probability. Bayesian econometrics takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters. A distinguishing feature of this book is its emphasis on classical and Markov chain Monte Carlo (MCMC) methods of simulation. The book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields. These include the linear regression model and extensions to Tobit, probit, and logit models; time series models; and models involving endogenous variables.
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 interests include dynamic macroeconomics as well as Bayesian econometrics.
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.
| Erscheint lt. Verlag | 8.10.2007 |
|---|---|
| Zusatzinfo | 15 Tables, unspecified |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 180 x 260 mm |
| Gewicht | 560 g |
| Themenwelt | Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie |
| ISBN-10 | 0-521-85871-2 / 0521858712 |
| ISBN-13 | 978-0-521-85871-7 / 9780521858717 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
