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Bayesian Inference in Dynamic Econometric Models Book

Bayesian Inference in Dynamic Econometric Models
Bayesian Inference in Dynamic Econometric Models, , Bayesian Inference in Dynamic Econometric Models has a rating of 3 stars
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  • Bayesian Inference in Dynamic Econometric Models
  • Written by author Luc Bauwens
  • Published by Oxford University Press, USA, March 2000
  • 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 n
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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 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


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