* بسم الله الرحمن الرحيم Econometric Theory And Methods Solutions Davidson & Mckinnon http://www.4shared.com/document/sdniBLDv/Econometric_Theory_And_Methods.htm (13.7 MB)
Table of contents: 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 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 Parameter Estimates 3.5 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 Linear 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 Tests 6.9 Final Remarks 6.10 Exercises 7 Generalized Least Squares and Related Topics 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-Average 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 Variables Estimation 8.1 Introduction 8.2 Correlation Between Error Terms and Regressors 8.3 Instrumental Variables Estimation 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 Regression Models 9.3 HAC Covariance Matrix Estimation 9.4 Tests Based on the GMM Criterion Function 9.5 GMM Estimators for Nonlinear Models 9.6 The Method of Simulated Moments 9.7 Final Remark 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 Tests 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 Responses 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 Remarks 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-Average Processes 13.3 Estimation AR, MA, and ARMA Models 13.4 Single-Equation Dynamic Models 13.5 Seasonality 13.6 Autoregressive Conditional Heteroskedasticity 13.7 Vector Autoregressions 13.8 Final Remarks 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 Correlation 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 Based 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 Regressors in Artificial Regressions 15.8 Exercises References Author Index Subject Index * -- والحمد لله، حمداً كثيراً طيباً مباركاً فيه كما يحب ربنا ويرضى سلمى أرنبة -- You received this message 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