Researchers in many fields are increasingly finding the Bayesian
approach to statistics to be an attractive one. This book
introduces the reader to the use of Bayesian methods in the field
of econometrics at the advanced undergraduate or graduate level.
The book is self-contained and does not require that readers have
previous training in econometrics. The focus is on models used by
applied economists and the computational techniques necessary to
implement Bayesian methods when doing empirical work. Topics
covered in the book include the regression model (and variants
applicable for use with panel data), time series models, models for
qualitative or censored data, nonparametric methods and Bayesian
model averaging. The book includes numerous empirical examples and
the website associated with it contains data sets and computer
programs to help the student develop the computational skills of
modern Bayesian econometrics.
Bayesian Econometrics introduces the reader to the use of Bayesian
methods in the field of econometrics at the advanced undergraduate
or graduate level. The book is self-contained and does not require
previous training in econometrics. The focus is on models used by
applied economists and the computational techniques necessary to
implement Bayesian methods when doing empirical work. It includes
numerous numerical examples and topics covered in the book
including the regression model (and variants applicable for use
with panel data time series models models for qualitative or
censored data nonparametric methods and Bayesian model averaging.
Gary Koop is Professor of Economics at the University of Glasgow.
Inhaltsangabe
From the contents: Preface 1 An Overview of Bayesian Econometrics - 2 The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable - 3 The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables - 4 The Normal Linear Regression Model with Other Priors - 5 The Nonlinear Regression Model - 6 The Linear Regression Model with General Error Covariance Matrix - 7 The Linear Regression Model with Panel Data - 8 Introduction to Time Series: State Space Models - 9 Qualitative and Limited Dependent Variable Models - 10 Flexible Models: Nonparametric and Semi-Parametric Methods - 11 Bayesian Model Averaging - 12 Other Models, Methods and Issues
Appendix A: Introduction to Matrix Algebra - Appendix B: Introduction to Probability and Statistics - Bibliography - IndexPreface. 1. An Overview of Bayesian Econometrics. 2. The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable. 3. The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables. 4. The Normal Linear Regression Model with Other Priors. 5. The Nonlinear Regression Model. 6. The Linear Regression Model with General Error Covariance Matrix. 7. The Linear Regression Model with Panel Data. 8. Introduction to Time Series: State Space Models. 9. Qualitative and Limited Dependent Variable Models. 10. Flexible Models: Nonparametric and Semi-Parametric Methods. 11. Bayesian Model Averaging. 12. Other Models, Methods and Issues. Appendix A: Introduction to Matrix Algebra. Appendix B: Introduction to Probability and Statistics. Bibliography. Index.
