A Guide to Modern Econometrics, Fifth Edition has become established as a highly successful textbook. It serves as a guide to alternative techniques in econometrics with an emphasis on intuition and the practical implementation of these approaches. This fifth edition builds upon the success of its predecessors. The text has been carefully checked and updated, taking into account recent developments and insights. It includes new material on casual inference, the use and limitation of p-values, instrumental variables estimation and its implementation, regression discontinuity design, standardized coefficients, and the presentation of estimation results.…mehr
A Guide to Modern Econometrics, Fifth Edition has become established as a highly successful textbook. It serves as a guide to alternative techniques in econometrics with an emphasis on intuition and the practical implementation of these approaches. This fifth edition builds upon the success of its predecessors. The text has been carefully checked and updated, taking into account recent developments and insights. It includes new material on casual inference, the use and limitation of p-values, instrumental variables estimation and its implementation, regression discontinuity design, standardized coefficients, and the presentation of estimation results.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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 Missing Data, Outliers and Influential Observations 2.10 Prediction Wrap-up Exercises 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 Wrap-up Exercises 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 Wrap-up Exercises 5 Endogenous Regressors, 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 Alternative Approaches to Estimate Causal Effects 5.6 The Generalized Instrumental Variables Estimator 5.7 Institutions and Economic Development 5.8 The Generalized Method of Moments 5.9 Illustration: Estimating Intertemporal Asset Pricing Models Wrap-up Exercises 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 Wrap-up Exercises 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 7.7.1 Regression-based Estimators 7.8 Duration Models Wrap-up Exercises 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 Illustration: The Persistence of Inflation 8.9 Forecasting with ARMA Models 8.10 Illustration: The Expectations Theory of the Term Structure 8.11 Autoregressive Conditional Heteroskedasticity 8.12 What about Multivariate Models? Wrap-up Exercises 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 Wrap-up Exercises 10 Models Based on Panel Data 10.1 Introduction to Panel Data Modelling 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 Panel Time Series 10.7 Models with Limited Dependent Variables 10.8 Incomplete Panels and Selection Bias 10.9 Pseudo Panels and Repeated Cross-sections Wrap-up 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 Bibliograph Index
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 Missing Data, Outliers and Influential Observations 2.10 Prediction Wrap-up Exercises 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 Wrap-up Exercises 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 Wrap-up Exercises 5 Endogenous Regressors, 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 Alternative Approaches to Estimate Causal Effects 5.6 The Generalized Instrumental Variables Estimator 5.7 Institutions and Economic Development 5.8 The Generalized Method of Moments 5.9 Illustration: Estimating Intertemporal Asset Pricing Models Wrap-up Exercises 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 Wrap-up Exercises 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 7.7.1 Regression-based Estimators 7.8 Duration Models Wrap-up Exercises 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 Illustration: The Persistence of Inflation 8.9 Forecasting with ARMA Models 8.10 Illustration: The Expectations Theory of the Term Structure 8.11 Autoregressive Conditional Heteroskedasticity 8.12 What about Multivariate Models? Wrap-up Exercises 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 Wrap-up Exercises 10 Models Based on Panel Data 10.1 Introduction to Panel Data Modelling 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 Panel Time Series 10.7 Models with Limited Dependent Variables 10.8 Incomplete Panels and Selection Bias 10.9 Pseudo Panels and Repeated Cross-sections Wrap-up 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 Bibliograph Index
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