Readership: Econometrics and statistics postgraduates. Professors and researchers in economics departments, business schools, statistics departments, or any research centre in the same fields, especially econometricians.
Luc Bauwens, Professor of Economics, Centre for Operations Research and Econometrics [CORE], Université Catholique de Louvain, Michel Lubrano, Directeur de Recherche, GREQAM, CNRS, and Jean-François Richard, University Professor of Economics, University of Pittsburgh
"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 applications to parameter estimation" - Paul Goodwin, International Journal 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
