Incluye referencias bibliográficas (páginas 477-492) e índice.

I. Statistical methods: 1. Statistical models: 1.1. Introduction ; 1.2. Sample, parameters, and sampling probability distributions ; 1.3. Independent and identically distributed models ; 1.4. Dominated models, likelihood function ; 1.5. Marginal and conditional models -- 2. Sequential models and Asympotics: 2.1. Introduction ; 2.2. Sequential stochastic models and Asympotics ; 2.3. Convergence in probability and almost sure ; 2.4. Convergence in distribution and central limit theorem ; 2.5. Noncausality and exogeneity in dynamic models -- 3. Estimation by maximization and by the method of moments: 3.1. Introduction ; 3.2. Estimation ; 3.3. Moment conditions and maximization ; 3.4. Estimation by the method of moments and generalized moments ; 3.5. Asymptotic properties of estimators -- 4. Asymptotic tests: 4.1. Introduction ; 4.2. Test and asymptotic tests ; 4.3. Wald tests ; 4.4. Rao test ; 4.5. Test based on the comparison of minima ; 4.6. Test based on maximum likelihood estimation ; 4.7. Hausman test ; 4.8. Encompassing test -- 5. Nonparametric methods: 5.1. Introduction ; 5.2. Empirical distribution and empirical distribution functions ; 5.3. Density estimation ; 5.4. Semiparametric methods -- 6. Simulation methods: 6.1. Introduction ; 6.2. Random number generators ; 6.3. Utilization in calculation procedures ; 6.4. Simulations and small sample properties of estimators and tests ; 6.5. Bootstrap and distribution of the moment estimators and the density -- II. Regression models: 7. Conditional expectation: 7.1. Introduction ; 7.2. Conditional expectations ; 7.3. Linear conditional expectation – 8. Univariate regression: 8.1. Introduction ; 8.2. Linear regression ; 8.3. Nonlinear parametric regression ; 8.4. Misspecified regression -- 9. Generalized least squares methods, Heteroskedasticity, and multivariate regression: 9.1. Introduction ; 9.2. Allowing for nuisance parameters in moment estimation ; 9.3. Heteroskedasticity ; 9.4. Multivariate regression -- 10. Nonparametric estimation of the regression: 10.1. Introduction ; 10.2. Estimation of the regression function by kernel ; 10.3. Estimating transformation of the regression function ; 10.4 Restrictions on the regression function -- 11. Discrete variables and partially observed models: 11.1. Introduction ; 11.2. Various types of models ; 11.3. Estimation -- III. Dynamic models: 12. Stationary dynamic models ; 12.1. Introduction ; 12.2. Second order processes ; 12.3. Gaussian processes ; 12.4. Spectral representations and autocovariance generating function ; 12.5. Filtering and forecasting ; 12.6. Stationary ARMA processes ; 12.7. Spectral representation of an ARMA (p, q) process ; 12.8. Estimation of ARMA models ; 12.9. Multivariate processes ; 12.10. Interpretation of a VAR(p) model under its MA(8) form ; 12.11. Estimation of VAR (p) models -- 13. Nonstationary processes and cointegration: 13.1. Introduction ; 13.2. Asymptotic properties of least squares ; 13.3. Analysis of cointegration and error correction -- 14. Models for conditional variance: 14.1. Introduction ; 14.2. Various types of ARCH models ; 14.3. Estimation method ; 14.4. Test for conditional homoskedasticity ; 14.5. Some specificities of ARCH-type models -- 15. Nonlinear dynamic models: 15.1. Introduction ; 15.2. Case where the conditional expectation is continuously differentiable ; 15.3. Case where the conditional expectation is not continuously differentiable: regime-switching models ; 15.4. Linearity test -- IV. Structural modeling: 16. Identification and overidentification in structural modeling: 16.1. Introduction ; 16.2. Structural model and reduced form ; 16.3. Identification: the example of simultaneous equations ; 16.4. Models from game theory ; 16.5. Overidentification -- 17. Simultaneity: 17.1. Introduction ; 17.2. Simultaneity and simultaneous equations ; 17.3. Endogeneity, exogeneity, and dynamic models ; 17.4. Simultaneity and selection bias ; 17.5. Instrumental variables estimation -- 18. Models with unobservable variables: 18.1. Introduction ; 18.2. Examples of models with unobservable variables ; 18.3. Comparison between structural model and reduced form ; 18.4. Identification problems ; 18.5. Estimation of models with unobservable variables ; 18.6. Counterfactuals and treatment effects.