Econometrics 12th Edition by Bruce Hansen ISBN 0691235899 9780691235899

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ISBN 10: 0691235899
ISBN 13: 9780691235899
Author: Bruce Hansen

Econometrics 12th Edition Table of contents:

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 theBook

Part 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

Part 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 TheBootstrap 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

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Tags: Bruce Hansen, Econometrics