This book offers an up-to-date coverage of the basic principles and tools of Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations, and the long available analytical results of Bayesian inference for linear regression models.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
`it can serve as a useful textbook for advanced undergraduate or graduate courses in either time series analysis or econometrics.'
Paul Goodwin, International Journal of Forecasting, 2000
`presents a comprehensive review of dynamic econometric models from a Bayesian perspective ... four insightful introductory chapters ... provide a valuable synthesis of current ideas and their application to parameter estimation.'
Paul Goodwin, International Journa of Forecasting, 2000
Chapter 1: Decision Theory and Bayesian Inference
Chapter 2: Bayesian Statistics and Linear Regression
Chapter 3: Methods of Numerical Integration
Chapter 4: Prior Densities for the Regression Model
Chapter 5: Dynamic Regression Models
Chapter 6: Bayesian Unit Roots
Chapter 7: Heteroskedasticity and ARCH
Chapter 8: Nonlinear Tome Series Models
Chapter 9: Systems of Equations
Appendix A: Probability Distributions
Appendix B: Generating Random Numbers