Informationen zum Autor Bruce E. Hansen is the Mary Claire Aschenbrener Phipps Distinguished Chair of Economics at the University of Wisconsin-Madison and one of the most cited econometricians in the world. Klappentext The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgroundsDraws on integrated, research-level datasets, provided on an accompanying websiteDiscusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learningFeatures hundreds of exercises that enable students to learn by doingIncludes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examplesCan serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen's Probability and Statistics for Economists Zusammenfassung The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds Draws on integrated, research-level datasets, provided on an accompanying website Discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning Features hundreds of exercises that enable students to learn by doing Includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples Can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen's Probability and Statistics for Economists Inhaltsverzeichnis PrefaceAcknowledgmentsNotation1 Introduction1.1 What Is Econometrics?1.2 The Probability Approach to Econometrics1.3 Econometric Terms1.4 Observational Data1.5 Standard Data Structures1.6 Econometric Software1.7 Replication1.8 Data Files for Textbook1.9 Reading the BookI Regression2 Conditional Expectation and Projection2.1 Introduction2.2 The Distribution of Wages2.3 Conditional Expectation2.4 Logs and Percentages2.5 Conditional Expectation Function2.6 Continuous Variables2.7 Law of Iterated Expectations2.8 CEF Error2.9 Intercept-Only Model2.10 Regression Variance2.11 Best Predictor2.12 Conditional Variance2.13 Homoskedasticity and Heteroskedasticity2.14 Regression Derivative2.15 Linear CEF2.16 Linear CEF with Nonlinear Effects2.17 Linear CEF with Dummy Variables2.18 Best Linear Predictor2.19 Illustrations of Best Linear Predictor2.20 Linear Predictor Error Variance2.21 Regression Coefficients2.22 Regression Subvecto...

Autorentext

Bruce E. Hansen

Klappentext

The most authoritative and up-to-date core econometrics textbook available

Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics.

• Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds
• Draws on integrated, research-level datasets, provided on an accompanying website
• Discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning
• Features hundreds of exercises that enable students to learn by doing
• Includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples
• Can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen's Probability and Statistics for Economists

Inhalt

PrefaceAcknowledgmentsNotation1 Introduction

• 1.1 What Is Econometrics?

• 1.2 The Probability Approach to Econometrics

• 1.3 Econometric Terms

• 1.4 Observational Data

• 1.5 Standard Data Structures

• 1.6 Econometric Software

• 1.7 Replication

• 1.8 Data Files for Textbook

• 1.9 Reading the BookI Regression2 Conditional Expectation and Projection

• 2.1 Introduction

• 2.2 The Distribution of Wages

• 2.3 Conditional Expectation

• 2.4 Logs and Percentages

• 2.5 Conditional Expectation Function

• 2.6 Continuous Variables

• 2.7 Law of Iterated Expectations

• 2.8 CEF Error

• 2.9 Intercept-Only Model

• 2.10 Regression Variance

• 2.11 Best Predictor

• 2.12 Conditional Variance

• 2.13 Homoskedasticity and Heteroskedasticity

• 2.14 Regression Derivative

• 2.15 Linear CEF

• 2.16 Linear CEF with Nonlinear Effects

• 2.17 Linear CEF with Dummy Variables

• 2.18 Best Linear Predictor

• 2.19 Illustrations of Best Linear Predictor

• 2.20 Linear Predictor Error Variance

• 2.21 Regression Coefficients

• 2.22 Regression Subvectors

• 2.23 Coefficient Decomposition

• 2.24 Omitted Variable Bias

• 2.25 Best Linear Approximation

• 2.26 Regression to the Mean

• 2.27 Reverse Regression

• 2.28 Limitations of the Best Linear Projection

• 2.29 Random Coefficient Model

• 2.30 Causal Effects

• 2.31 Existence and Uniqueness of the Conditional Expectation*

• 2.32 Identification*

• 2.33 Technical Proofs*

• 2.34 Exercises3 The Algebra of Least Squares

• 3.1 Introduction

• 3.2 Samples

• 3.3 Moment Estimators

• 3.4 Least Squares Estimator

• 3.5 Solving for Least Squares with One Regressor

• 3.6 Solving for Least Squares with Multiple Regressors

• 3.7 Illustration

• 3.8 Least Squares Residuals

• 3.9 Demeaned Regressors

• 3.10 Model in Matrix Notation

• 3.11 Projection Matrix

• 3.12 Annihilator Matrix

• 3.13 Estimation of Error Variance

• 3.14 Analysis of Variance

• 3.15 Projections

• 3.16 Regression Components

• 3.17 Regression Components (Alternative Derivation)*

• 3.18 Residual Regression

• 3.19 Leverage Values

• 3.20 Leave-One-Out Regression

• 3.21 Influential Observations

• 3.22 CPS Dataset

• 3.23 Numerical Computation

• 3.24 Collinearity Errors

• 3.25 Programming

• 3.26 Exercises4 Least Squares Regression

• 4.1 Introduction

• 4.2 Random Sampling

• 4.3 Sample Mean

• 4.4 Linear Regression Model

• 4.5 Expectation of Least Squares Estimator

• 4.6 Variance of Least Squares Estimator

• 4.7 Unconditional Moments

• 4.8 Gauss-Markov Theorem

• 4.9 Generalized Least Squares

• 4.10 Residuals

• 4.11 Estimation of Error Variance

• 4.12 Mean-Squared Forecast Error

• 4.13 Covar…