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Estimation and Inference in Econometrics 

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Comment from the Stata technical groupEstimation and Inference in Econometrics is a book that every serious student of econometrics should keep within arm’s reach. Davidson and MacKinnon provide a rather atypical insight into the theory and practice of econometrics. By itself, their exposition of the many uses of artificial regressions makes the book a valuable addition to any econometrician’s library. Suitable for graduate courses in econometrics, advanced selfstudy, or as a desk reference, this book receives our highest recommendation. 

Table of contentsView table of contents >> 1 The Geometry of Least Square
1.1 Introduction
1.2 The Geometry of Least Squares 1.3 Restrictions and Reparametrizations 1.4 The FrischWaughLovell Theorem 1.5 Computing OLS Estimates 1.6 Influential Observations and Leverage 1.7 Further Reading and Conclusion 2 Nonlinear Regression Models and Nonlinear Least Squares
2.1 Introduction
2.2 The Geometry of Nonlinear Least Squares 2.3 Identification in Nonlinear Regression Models 2.4 Models and DataGenerating Processes 2.5 Linear and Nonlinear Regression Functions 2.6 Error Terms 2.7 Conclusion 3 Inference in Nonlinear Regression Models
3.1 Introduction
3.2 Covariance Matrix Estimation 3.3 Confidence Intervals and Confidence Regions 3.4 Hypothesis Testing: Introduction 3.5 Hypothesis Testing in Linear Regression Models 3.6 Hypothesis Testing in Nonlinear Regression Models 3.7 Restrictions and Pretest Estimators Conclusion 4 Introduction to Asymptotic Theory and Methods
4.1 Introduction
4.2 Sequences, Limits, and Convergence 4.3 Rates of Convergence 4.4 DataGenerating Processes and Asymptotic Theory 4.5 Consistency and Laws of Large Numbers 4.6 Asymptotic Normality and Central Limit Theorems 4.7 Some Useful Results 4.8 Conclusion 5 Asymptotic Methods and Nonlinear Least Squares
5.1 Introduction
5.2 Asymptotic Identifiability 5.3 Consistency of the NLS Estimator 5.4 Asymptotic Normality of the NLS Estimator 5.5 Asymptotic Efficiency of Nonlinear Least Squares 5.6 Properties of Nonlinear Least Squares Residuals 5.7 Test Statistics Based on NLS Estimates 5.8 Further Reading and Conclusion 6 The Gauss–Newton Regression
6.1 Introduction
6.2 Computing Covariance Matrices 6.3 Collinearity in Nonlinear Regression Models 6.4 Testing Restrictions 6.5 Diagnostic Tests for Linear Regression Models 6.6 OneStep Efficient Estimation 6.7 Hypothesis Tests Using Any Consistent Estimates 6.8 Nonlinear Estimation Using the GNR 6.9 Further Reading 7 Instrumental Variables
7.1 Introduction
7.2 Errors in Variables 7.3 Simultaneous Equations 7.4 Instrumental Variables: The Linear Case 7.5 TwoStage Least Squares 7.6 Instrumental Variables: The Nonlinear Case 7.7 Hypothesis Tests Based on the GNR 7.8 Identification and Overidentifying Restrictions 7.9 DurbinWuHausman Tests 7.10 Conclusion 8 The Method of Maximum Likelihood
8.1 Introduction
8.2 Fundamental Concepts and Notation 8.3 Transformations and Reparametrizations 8.4 Consistency 8.5 The Asymptotic Distribution of the ML Estimator 8.6 The Information Matrix Equality 8.7 Concentrating the Loglikelihood Function 8.8 Asymptotic Efficiency of the ML Estimator 8.9 The Three Classical Test Statistics 8.10 Nonlinear Regression Models 8.11 Conclusion 9 Maximum Likelihood and Generalized Least Squares
9.1 Introduction
9.2 Generalized Least Squares 9.3 The Geometry of GLS 9.4 The GaussNewton Regression 9.5 Feasible Generalized Least Squares 9.6 Maximum Likelihood and GNLS 9.7 Introduction to Multivariate Regression Models 9.8 GLS Estimation of Multivariate Regression Models 9.9 ML Estimation of Multivariate Regression Models 9.10 Modeling TimeSeries/CrossSection Data 9.11 Conclusion 10 Serial Correlation
10.1 Introduction
10.2 Serial Correlation and Least Squares Estimation 10.3 Estimating Regression Models with AR(1) Errors 10.4 Standard Errors and Covariance Matrices 10.5 HigherOrder AR Processes 10.6 Initial Observations in Models with AR Errors 10.7 Moving Average and ARMA Processes 10.8 Testing for Serial Correlation 10.9 Common Factor Restrictions 10.10 Instrumental Variables and Serial Correlation 10.11 Serial Correlation and Multivariate Models 10.12 Conclusion 11 Tests Based on the GaussNewton Regression
11.1 Introduction
11.2 Tests for Equality of Two Parameter Vectors 11.3 Testing Nonnested Regression Models 11.4 Tests Based on Comparing Two Sets of Estimates 11.5 Testing for Heteroskedasticity 11.6 A HeteroskedasticityRobust Version of the GNR 11.7 Conclusion 12 Interpreting Tests in Regression Directions
12.1 Introduction
12.2 Size and Power 12.3 Drifting DGPs 12.4 The Asymptotic Distribution of Test Statistics 12.5 The Geometry of Test Power 12.6 Asymptotic Relative Efficiency 12.7 Interpreting Test Statistics that Reject the Null 12.8 Test Statistics that Do Not Reject the Null 12.9 Conclusion 13 The Classical Hypothesis Tests
13.1 Introduction
13.2 The Geometry of the Classical Test Statistics 13.3 Asymptotic Equivalence of the Classical Tests 13.4 Classical Tests and Linear Regression Models 13.5 Alternative Covariance Matrix Estimators 13.6 Classical Test Statistics 13.7 The OuterProductoftheGradient Regression 13.8 Further Reading and Conclusion 14 Transforming the Dependent Variable
14.1 Introduction
14.2 The BoxCox Transformation 14.3 The Role of Jacobian Terms in ML Estimation 14.4 DoubleLength Artificial Regressions 14.5 The DLR and Models Involving Transformations 14.6 Testing Linear and Loglinear Regression Models 14.7 Other Transformations 14.8 Conclusion 15 Qualitative and Limited Dependent Variables
15.1 Introduction
15.2 Binary Response Models 15.3 Estimation of Binary Response Models 15.4 An Artificial Regression 15.5 Models for More than Two Discrete Responses 15.6 Models for Truncated Data 15.7 Models for Censored Data 15.8 Sample Selectivity 15.9 Conclusion 16 Heteroskedasticity and Related Topics
16.1 Introduction
16.2 Least Squares and Heteroskedasticity 16.3 Covariance Matrix Estimation 16.4 Autoregressive Conditional Heteroskedasticity 16.5 Testing for Heteroskedasticity 16.6 Skedastic Directions and Regression Directions 16.7 Tests for Skewness and Excess Kurtosis 16.8 Conditional Moment Tests 16.9 Information Matrix Tests 16.10 Conclusion 17 The Generalized Method of Moments
17.1 Introduction and Definitions
17.2 Criterion Functions and MEstimators 17.3 Efficient GMM Estimators 17.4 Estimation with Conditional Moments 17.5 Covariance Matrix Estimation 17.6 Inference with GMM Models 17.7 Conclusion 18 Simultaneous Equations Models
18.1 Introduction
18.2 Exogeneity and Causality 18.3 Identification in Simultaneous Equations Models 18.4 FullInformation Maximum Likelihood 18.5 LimitedInformation Maximum Likelihood 18.6 ThreeStage Least Squares 18.7 Nonlinear Simultaneous Equations Models 18.8 Conclusion 19 Regression Models for TimeSeries Data
19.1 Introduction
19.2 Spurious Regressions 19.3 Distributed Lags 19.4 Dynamic Regression Models 19.5 Vector Autoregressions 19.6 Seasonal Adjustment 19.7 Modeling Seasonality 19.8 Conclusion 20 Unit Roots and Cointegration
20.1 Introduction
20.2 Testing for Unit Roots 20.3 Asymptotic Theory for Unit Root Tests 20.4 Serial Correlation and Other Problems 20.5 Cointegration 20.6 Testing for Cointegration 20.7 ModelBuilding with Cointegrated Variables 20.8 Vector Autoregressions and Cointegration 20.9 Conclusion 21 Monte Carlo Experiments
21.1 Introduction
21.2 Generating PseudoRandom Numbers 21.3 Generating PseudoRandom Variates 21.4 Designing Monte Carlo Experiments 21.5 Variance Reduction: Antithetic Variates 21.6 Variance Reduction: Control Variates 21.7 Response Surfaces 21.8 The Bootstrap and Related Methods 21.9 Conclusion Appendices
A Matrix Algebra
A.1 Introduction
A.2 Elementary Facts about Matrices A.3 The Geometry of Vectors A.4 Matrices as Mappings of Linear Spaces A.5 Partitioned Matrices A.6 Determinants A.7 Positive Definite Matrices A.8 Eigenvalues and Eigenvectors B Results from Probability Theory
B.1 Introduction
B.2 Random Variables and Probability Distributions B.3 Moments of Random Variables B.4 Some Standard Probability Distributions References
Author Index
Subject Index

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