Table of Contents
Preface.
1 Introduction.
1.1 About Econometrics.
1.2 The Structure of this Book.
1.3 Illustrations and Exercises.
2 An Introduction to Linear Regression.
2.1 Ordinary Least Squares as an Algebraic Tool.
2.2 The Linear Regression Model.
2.3 Small Sample Properties of the OLS Estimator.
2.4 Goodness-of-fit.
2.5 Hypothesis Testing.
2.6 Asymptotic Properties of the OLS Estimator.
2.7 Illustration: The Capital Asset Pricing Model.
2.8 Multicollinearity.
2.9 Prediction.
3 Interpreting and Comparing Regression Models.
3.1 Interpreting the Linear Model.
3.2 Selecting the Set of Regressors.
3.3 Misspecifying the Functional Form.
3.4 Illustration: Explaining House Prices.
3.5 Illustration: Predicting Stock Index Returns.
3.6 Illustration: Explaining Individual Wages.
4 Heteroskedasticity and Autocorrelation.
4.1 Consequences for the OLS Estimator.
4.2 Deriving an Alternative Estimator.
4.3 Heteroskedasticity.
4.4 Testing for Heteroskedasticity.
4.5 Illustration: Explaining Labour Demand.
4.6 Autocorrelation.
4.7 Testing for First-order Autocorrelation.
4.8 Illustration: The Demand for Ice Cream.
4.9 Alternative Autocorrelation Patterns.
4.10 What to do When you Find Autocorrelation?
4.11 Illustration: Risk Premia in Foreign Exchange Markets.
5 Endogeneity, Instrumental Variables and GMM.
5.1 A Review of the Properties of the OLS Estimator.
5.2 Cases Where the OLS Estimator Cannot be Saved.
5.3 The Instrumental Variables Estimator.
5.4 Illustration: Estimating the Returns to Schooling.
5.5 The Generalized Instrumental Variables Estimator.
5.6 The Generalized Method of Moments.
5.7 Illustration: Estimating Intertemporal Asset Pricing Models.
5.8 Concluding Remarks.
6 Maximum Likelihood Estimation and Specification Tests.
6.1 An Introduction to Maximum Likelihood.
6.2 Specification Tests.
6.3 Tests in the Normal Linear Regression Model.
6.4 Quasi-maximum Likelihood and Moment Conditions Tests.
7 Models with Limited Dependent Variables.
7.1 Binary Choice Models.
7.2 Multiresponse Models.
7.3 Models for Count Data.
7.4 Tobit Models.
7.5 Extensions of Tobit Models.
7.6 Sample Selection Bias.
7.7 Estimating Treatment Effects.
7.8 Duration Models.
8 Univariate Time Series Models.
8.1 Introduction.
8.2 General ARMA Processes.
8.3 Stationarity and Unit Roots.
8.4 Testing for Unit Roots.
8.5 Illustration: Long-run Purchasing Power Parity (Part 1).
8.6 Estimation of ARMA Models.
8.7 Choosing a Model.
8.8 Predicting with ARMA Models.
8.9 Illustration: The Expectations Theory of the Term Structure.
8.10 Autoregressive Conditional Heteroskedasticity.
8.11 What about Multivariate Models?
9 Multivariate Time Series Models.
9.1 Dynamic Models with Stationary Variables.
9.2 Models with Nonstationary Variables.
9.3 Illustration: Long-run Purchasing Power Parity (Part 2).
9.4 Vector Autoregressive Models.
9.5 Cointegration: the Multivariate Case.
9.6 Illustration: Money Demand and Inflation.
9.7 Concluding Remark.
10 Models Based on Panel Data.
10.1 Introduction to Panel Data Modeling.
10.2 The Static Linear Model.
10.3 Illustration: Explaining Individual Wages.
10.4 Dynamic Linear Models.
10.5 Illustration: Explaining Capital Structure.
10.6 Nonstationarity, Unit Roots and Cointegration.
10.7 Models with Limited Dependent Variables.
10.8 Incomplete Panels and Selection Bias.
10.9 Pseudo Panels and Repeated Cross-sections.
A Vectors and Matrices.
A.1 Terminology.
A.2 Matrix Manipulations.
A.3 Properties of Matrices and Vectors.
A.4 Inverse Matrices.
A.5 Idempotent Matrices.
A.6 Eigenvalues and Eigenvectors.
A.7 Differentiation.
A.8 Some Least Squares Manipulations.
B Statistical and Distribution Theory.
B.1 Discrete Random Variables.
B.2 Continuous Random Variables.
B.3 Expectations and Moments.
B.4 Multivariate Distributions.
B.5 Conditional Distributions.
B.6 The Normal Distribution.
B.7 Related Distributions
Bibliography.
Index.