Bruce Hansen: Econometrics | Buch

Cover: 9780691235899 | Econometrics | Bruce Hansen | Buch | Einband - fest (Hardcover) | 2022

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Beschreibung

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

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

Über den Autor

Bruce E. Hansen

Inhaltsverzeichnis

Preface
Acknowledgments
Notation
1 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 Book
I Regression
- 2 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 Exercises
- 3 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 Exercises
- 4 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 Covariance Matrix Estimation under Homoskedasticity
  - 4.14 Covariance Matrix Estimation under Heteroskedasticity
  - 4.15 Standard Errors
  - 4.16 Estimation with Sparse Dummy Variables
  - 4.17 Computation
  - 4.18 Measures of Fit
  - 4.19 Empirical Example
  - 4.20 Multicollinearity
  - 4.21 Clustered Sampling
  - 4.22 Inference with Clustered Samples
  - 4.23 At What Level to Cluster?
  - 4.24 Technical Proofs*
  - 4.25 Exercises
- 5 Normal Regression
  - 5.1 Introduction
  - 5.2 The Normal Distribution
  - 5.3 Multivariate Normal Distribution
  - 5.4 Joint Normality and Linear Regression
  - 5.5 Normal Regression Model
  - 5.6 Distribution of OLS Coefficient Vector
  - 5.7 Distribution of OLS Residual Vector
  - 5.8 Distribution of Variance Estimator
  - 5.9 t-Statistic
  - 5.10 Confidence Intervals for Regression Coefficients
  - 5.11 Confidence Intervals for Error Variance
  - 5.12 t-Test
  - 5.13 Likelihood Ratio Test
  - 5.14 Information Bound for Normal Regression
  - 5.15 Exercises
- II Large Sample Methods
  - 6 A Review of Large Sample Asymptotics
    - 6.1 Introduction
    - 6.2 Modes of Convergence
    - 6.3 Weak Law of Large Numbers
    - 6.4 Central Limit Theorem
    - 6.5 Continuous Mapping Theorem and Delta Method
    - 6.6 Smooth Function Model
    - 6.7 Stochastic Order Symbols
    - 6.8 Convergence of Moments
  - 7 Asymptotic Theory for Least Squares
    - 7.1 Introduction
    - 7.2 Consistency of Least Squares Estimator
    - 7.3 Asymptotic Normality
    - 7.4 Joint Distribution
    - 7.5 Consistency of Error Variance Estimators
    - 7.6 Homoskedastic Covariance Matrix Estimation
    - 7.7 Heteroskedastic Covariance Matrix Estimation
    - 7.8 Summary of Covariance Matrix Notation
    - 7.9 Alternative Covariance Matrix Estimators*
    - 7.10 Functions of Parameters
    - 7.11 Asymptotic Standard Errors
    - 7.12 t-Statistic
    - 7.13 Confidence Intervals
    - 7.14 Regression Intervals
    - 7.15 Forecast Intervals
    - 7.16 Wald Statistic
    - 7.17 Homoskedastic Wald Statistic
    - 7.18 Confidence Regions
    - 7.19 Edgeworth Expansion*
    - 7.20 Uniformly Consistent Residuals*
    - 7.21 Asymptotic Leverage*
    - 7.22 Exercises
  - 8 Restricted Estimation
    - 8.1 Introduction
    - 8.2 Constrained Least Squares
    - 8.3 Exclusion Restriction
    - 8.4 Finite Sample Properties
    - 8.5 Minimum Distance
    - 8.6 Asymptotic Distribution
    - 8.7 Variance Estimation and Standard Errors
    - 8.8 Efficient Minimum Distance Estimator
    - 8.9 Exclusion Restriction Revisited
    - 8.10 Variance and Standard Error Estimation
    - 8.11 Hausman Equality
    - 8.12 Example: Mankiw, Romer, and Weil (1992)
    - 8.13 Misspecification
    - 8.14 Nonlinear Constraints
    - 8.15 Inequality Restrictions
    - 8.16 Technical Proofs*
    - 8.17 Exercises
  - 9 Hypothesis Testing
    - 9.1 Introduction
    - 9.2 Hypotheses
    - 9.3 Acceptance and Rejection
    - 9.4 Type I Error
    - 9.5 T-Tests
    - 9.6 Type II Error and Power
    - 9.7 Statistical Significance
    - 9.8 p-Values
    - 9.9 t-Ratios and the Abuse of Testing
    - 9.10 Wald Tests
    - 9.11 Homoskedastic Wald Tests
    - 9.12 Criterion-Based Tests
    - 9.13 Minimum Distance Tests
    - 9.14 Minimum Distance Tests under Homoskedasticity
    - 9.15 F Tests
    - 9.16 Hausman Tests
    - 9.17 Score Tests
    - 9.18 Problems with Tests of Nonlinear Hypotheses
    - 9.19 Monte Carlo Simulation
    - 9.20 Confidence Intervals by Test Inversion
    - 9.21 Multiple Tests and Bonferroni Corrections
    - 9.22 Power and Test Consistency
    - 9.23 Asymptotic Local Power
    - 9.24 Asymptotic Local Power, Vector Case
    - 9.25 Exercises
  - 10 Resampling Methods
    - 10.1 Introduction
    - 10.2 Example
    - 10.3 Jackknife Estimation of Variance
    - 10.4 Example
    - 10.5 Jackknife for Clustered Observations
    - 10.6 The Bootstrap Algorithm
    - 10.7 Bootstrap Variance and Standard Errors
    - 10.8 Percentile Interval
    - 10.9 The Bootstrap Distribution
    - 10.10 The Distribution of the Bootstrap Observations
    - 10.11 The Distribution of the Bootstrap Sample Mean
    - 10.12 Bootstrap Asymptotics
    - 10.13 Consistency of the Bootstrap Estimate of Variance
    - 10.14 Trimmed Estimator of Bootstrap Variance
    - 10.15 Unreliability of Untrimmed Bootstrap Standard Errors
    - 10.16 Consistency of the Percentile Interval
    - 10.17 Bias-Corrected Percentile Interval
    - 10.18 BCa Percentile Interval
    - 10.19 Percentile-t Interval
    - 10.20 Percentile-t Asymptotic Refinement
    - 10.21 Bootstrap Hypothesis Tests
    - 10.22 Wald-Type Bootstrap Tests
    - 10.23 Criterion-Based Bootstrap Tests
    - 10.24 Parametric Bootstrap
    - 10.25 How Many Bootstrap Replications?
    - 10.26 Setting the Bootstrap Seed
    - 10.27 Bootstrap Regression
    - 10.28 Bootstrap Regression Asymptotic Theory
    - 10.29 Wild Bootstrap
    - 10.30 Bootstrap for Clustered Observations
    - 10.31 Technical Proofs*
    - 10.32 Exercises
  - III Multiple Equation Models
    - 11 Multivariate Regression
      - 11.1 Introduction
      - 11.2 Regression Systems
      - 11.3 Least Squares Estimator
      - 11.4 Expectation and Variance of Systems Least Squares
      - 11.5 Asymptotic Distribution
      - 11.6 Covariance Matrix Estimation
      - 11.7 Seemingly Unrelated Regression
      - 11.8 Equivalence of SUR and Least Squares
      - 11.9 Maximum Likelihood Estimator
      - 11.10 Restricted Estimation
      - 11.11 Reduced Rank Regression
      - 11.12 Principal Component Analysis
      - 11.13 Factor Models
      - 11.14 Approximate Factor Models
      - 11.15 Factor Models with Additional Regressors
      - 11.16 Factor-Augmented Regression
      - 11.17 Multivariate Normal*
      - 11.18 Exercises
    - 12 Instrumental Variables
      - 12.1 Introduction
      - 12.2 Overview
      - 12.3 Examples
      - 12.4 Endogenous Regressors
      - 12.5 Instruments
      - 12.6 Example: College Proximity
      - 12.7 Reduced Form
      - 12.8 Identification
      - 12.9 Instrumental Variables Estimator
      - 12.10 Demeaned Representation
      - 12.11 Wald Estimator
      - 12.12 Two-Stage Least Squares
      - 12.13 Limited Information Maximum Likelihood
      - 12.14 Split-Sample IV and JIVE
      - 12.15 Consistency of 2SLS
      - 12.16 Asymptotic Distribution of 2SLS
      - 12.17 Determinants of 2SLS...

Details

Erscheinungsjahr:	2022
Fachbereich:	Volkswirtschaft
Genre:	Importe , Wirtschaft
Rubrik:	Recht & Wirtschaft
Medium:	Buch
Inhalt:	Einband - fest (Hardcover)
ISBN-13:	9780691235899
ISBN-10:	0691235899
Sprache:	Englisch
Einband:	Gebunden
Autor:	Hansen, Bruce
Hersteller:	Princeton University Press
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	259 x 211 x 63 mm
Von/Mit:	Bruce Hansen
Erscheinungsdatum:	16.08.2022
Gewicht:	2,316 kg