'This is a clear, concise, and, above all, practical introduction to Bayesian econometrics. Graduate and advanced undergraduate students will find here a self-contained introduction to Bayesian theory, computation, and applied econometric modeling that can accompany them well into their studies.' William J. McCausland, Université de Montréal
1. The subjective interpretation of probability; 2. Bayesian inference; 3. Point estimation; 4. Frequentist properties of Bayesian estimators; 5. Interval estimation; 6. Hypothesis testing; 7. Prediction; 8. Choice of prior; 9. Asymptotic Bayes; 10. The linear regression model; 11. Basics of random variate generation and posterior simulation; 12. Posterior simulation via Markov chain Monte Carlo; 13. Hierarchical models; 14. Latent variable models; 15. Mixture models; 16. Bayesian methods for model comparison, selection and big data; 17. Univariate time series methods; 18. State space and unobserved components models; 19. Time series models for volatility; 20. Multivariate time series methods; Appendix; Bibliography; Index.