Incluye referencias bibliográficas (páginas 702-721)

Preface -- Data, solutions, and corrections -- 1 Regression models: 1.1 Introduction ; 1.2 Distributions, densities, and moments ; 1.3 The specification of regression models ; 1.4 Matrix algebra ; 1.5 Method of moments estimation ; 1.6 Notes on the exercises ; 1.7 Exercises – 2 The geometry of linear regression: 2.1 Introduction ; 2.2 The geometry of vector spaces ; 2.3 The geometry of OLS estimation ; 2.4 The Frisch-waugh-lovell theorem ; 2.5 Applications of the FWL theorem M 2.6 Influential observations and leverage ; 2.7 Final remarks ; 2.8 Exercises -- 3 The statistical properties of ordinary least squares: 3.1 Introduction ; 3.2 Are ols parameter estimators unbiased? ; 3.3 Are OLS parameter estimators consistent? ; 3.4 The covariance matrix of the ols parmeter estimates ; Efficiency of the OLS estimator ; 3.6 Residuals and error terms ; 3.7 Misspecification of linear regression models ; 3.8 Measures of goodness of fit ; 3.9 Final remarks ; 3.10 Exercises -- 4 Hypothesis testing in linear regression models: 4.1 Introduction ; 4.2 Basic ideas ; 4.3 Some common distributions ; 4.4 Exact tests in the classical normal linear model ; 4.5 Large-sample tests in llinear regression models ; 4.6 Simulation-based tests ; 4.7 The power of hypothesis tests ; 4.8 Final remarks ; 4.9 Exercises -- 5 Confidence intervals: 5.1 Introduction ; 5.2 Exact and asymptotic confidence intervals ; 5.3 Bootstrap confidence intervals ; 5.4 Confidence regions ; 5.5 Heteroskedasticity-consistent covariance matrices ; 5.6 The delta method ; 5.7 Final remarks ; 5.8 Exercises – 6 Nonlinear regression: 6.1 Introduction ; 6.2 Method-of-moments estimators for nonlinear models ; 6.3 Nonlinear least squares ; 6.4 Computing NLS estimates ; 6.5 The gauss-newton regression ; 6.6 One-step estimation ; 6.7 Hypothesis testing ; 6.8 Heteroskedasticity-robust test ; 6.9 Final remarks ; 6.10 Exercises – 7 Generalized least squares and related tipics: 7.1 Introduction ; 7.2 The GLS estimator ; 7.3 Computing GLS estimates ; 7.4 Feasible generalized least squares ; 7.5 Heteroskedasticity ; 7.6 Autoregressive and moving a verage processes ; 7.7 Testing for serial correlation ; 7.8 Estimating models with autoregressive errors ; 7.9 Specification testing and serial correlation ; 7.10 Models for panel data ; 7.11 Final remarks ; 7.12 Exercises -- 8 Instrumental: 8.1 Introduction ; 8.2 Correlation between error terms and regressors ; 8.3 Instrumental variables estimations ; 8.4 Finite-sample properties of IV estimators ; 8.5 Hypothesis testing ; 8.6 Testing overidentifying restrictions ; 8.7 Durbin-wu-hausman tests ; 8.8 Bootstrap tests ; 8.9 IV estimation of nonlinear models ; 8.10 Final remarks ; 8.11 Exercises -- 9 The generalized method of moments: 9.1 Introduction ; 9.2 GMM estimators for linear refression models ; 9.3 GAC covariance matrix estimation ; 9.4 Tests based on the GMM criterion fuction ; 9.5 GMM estimators for nonlinear models ; 9.6 The method of simulated moments ; 9.7 Final remarks ; 9.8 Exercises -- 10 The method of maximum likelihood: 10.1 Introduction ; 10.2 Basic concepts of maximum likelihood estimation ; 10.3 Asymptotic properties of ML estimators ; 10.4 The covariance matrix of the ML estimator ; 10.5 Hypothesis testing ; 10.6 The asymptotic theory of the three classical test ; 10.7 ML estimation of models with autoregressive errors ; 10.8 Transformations of the dependent variable ; 10.9 Final remarks ; 10.10 Exercises -- 11 Discrete and limited dependent variables: 11.1 Introduction ; 11.2 Binary response models: estimation ; 11.3 Binary response models: inference ; 11.4 Models for more than two discrete respondes ; 11.5 Models for count data ; 11.6 Models for censored and truncated data ; 11.7 Sample selectivity ; 11.8 Duration models ; 11.9 Final remarks ; 11.10 Exercises -- 12 Multivariate models: 12.1 Introduction ; 12.2 Seemingly unrelated linear regressions ; 12.3 Systems of nonlinear regressions ; 12.4 Linear simultaneous equations models ; 12.5 Maximum likelihood estimation ; 12.6 Nonlinear simultaneous equations models ; 12.7 Final remakrs ; 12.8 Appendix: detailed results on FIML and LIML ; 12.9 Exercises -- 13 Methods for stationary time-series data: 13.1 Introduction ; 13.2 Autoregressive and moving-a verage processes ; 13.3 Estimatinf AR, MA, and ARMA models ; 13.4 Single-equation dynamic models ; 13.5 Seasonality ; 13.6 Autoregressive condictional heteroscedasticity ; 13.7 Vector autoregressions ; 13.8 Final remakrs ; 13.9 Exercises -- 14 Unit roots and cointegration: 14.1 Introduction ; 14.2 Random walks and unit roots ; 14.3 Unit root tests ; 14.4 Serial correlations and unit root tests ; 14.5 Cointegration ; 14.6 Testing for cointegration ; 14.7 Final remarks ; 14.8 Exercises -- 15 Testing the specification of econometric models: 15.1 Introduction ; 15.2 Specification tests bases on artificial regressions ; 15.3 Nonnested hypothesis tests ; 15.4 Model selection based on information criteria ; 15.5 Nonparametric estimation : 15.6 Final remarks ; 15.7 Appendix: test refressors in artificial regressions ; 15.8 Exercises -- References -- Author index -- Subjetc index.