Incluye referencias bibliográficas (páginas 383-3899 e índice.

1. The Bayesian algorithm: 1.1. Econometric analysis ; 1.2. Statistical analysis ; 1.3. Bayes’ theorem ; 1.4. The components of Bayes’ theorem ; 1.5. Conclusion and summary ; 1.6. Exercises and complements ; 1.7. Appendix to chapter 1: some probability distributions -- 2. Prediction and model criticism: 2.1. Methods of model checking ; 2.2. Informal model checks ; 2.3. Uncheckable beliefs? ; 2.4. Formal model checks ; 2.5. Posterior prediction ; 2.6. Posterior odds and model choice ; 2.7. Enlarging the model ; 2.8. Summary ; 2.9. Exercises -- 3. Linear regression models: 3.1. Introduction ; 3.2. Economists and regression models ; 3.3. Linear regression models ; 3.4. A multinomial approach to linear regression ; 3.5. Checking the normal linear model ; 3.6. Extending the normal linear model ; 3.7. Conclusion and summary of the argument ; 3.8. Appendix to chapter 3 ; 3.9. Exercises and complements -- 4. Bayesian calculations: 4.1. Normal approximations ; 4.2. Exact sampling in one step ; 4.3. Markov chain Monte Carlo ; 4.4. Two general methods of constructing Kernels ; 4.5. Conclusion ; 4.6. Exercises and complements -- 5. Non-linear regression models: 5.1. Estimation of production functions ; 5.2. Binary choice ; 5.3. Ordered multinomial choice ; 5.4. Multinomial choice ; 5.5. Tobit models ; 5.6. Count data ; 5.7. Duration data ; 5.8. Concluding remarks ; 5.9. Exercises ; 5.10. Appendix to chapter 5: some further distributions -- 6. Randomized, controlled and observational data: 6.1. Introduction ; 6.2. Designed experiments ; 6.3. Simson’s Paradox ; 6.4. Conclusions ; 6.5. Appendix to chapter 6: Koopmans’ on exogeneity -- 7. Models for panel data: 7.1. Panel data ; 7.2. How do panels help? ; 7.3. Linear models on panel data ; 7.4. Panel counts ; 7.5. Panel duration data ; 7.6. Panel binary data ; 7.7. Concluding remarks ; 7.8. Exercises -- 8. Instrumental variables: 8.1. Introduction ; 8.2. Randomizers and instruments ; 8.3. Models and instrumental variables ; 8.4. The structure of a recursive equations model ; 8.5. Inference in a recursive equations model ; 8.5. Inference in a recursive systems ; 8.6. A numerical study of inference with instrumental variables ; 8.7. An application of IV methods to wages and education ; 8.8. Simultaneous equations ; 8.9. Concluding remarks about instrumental variables -- 9. Some time series models: 9.1. First order autoregressions ; 9.2. Stochastic volatility ; 9.3. Extensions ; 9.4. Exercises -- Appendix 1. A conversion manual – Appendix 2. Programming -- Appendix 3. BUGS code.