Introduction to Econometrics, Global Edition
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- HAG303 Hagrannsóknir I.
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For courses in introductory econometrics. Engaging applications bring the theory and practice of modern econometrics to life Ensure students grasp the relevance of econometrics with Introduction to Econometrics -- the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition , Global Edition, maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around.
The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics.
Pearson MyLab™ Economics is not included. Students, if Pearson MyLab Economics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Economics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. Reach every student by pairing this text with Pearson MyLab Economics MyLab™ is the teaching and learning platform that empowers you to reach every student.
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Introduction to Econometrics, Global EditionAnnað
- Höfundar: James H. Stock, Mark W. Watson
- Útgáfa:4
- Útgáfudagur: 21-02-2019
- Engar takmarkanir á útprentun
- Engar takmarkanir afritun
- Format:Page Fidelity
- ISBN 13: 9781292264523
- Print ISBN: 9781292264455
- ISBN 10: 1292264527
Efnisyfirlit
- Title Page
- Copyright Page
- Brief Contents
- Contents
- Key Concepts
- General Interest Boxes
- Preface
- Acknowledgments
- Global Acknowledgments
- Chapter 1: Economic Questions and Data
- 1.1. Economic Questions We Examine
- Question #1: Does Reducing Class Size Improve Elementary School Education?
- Question #2: Is There Racial Discrimination in the Market for Home Loans?
- Question #3: Does Healthcare Spending Improve Health Outcomes?
- Question #4: By How Much Will U.S. GDP Grow Next Year?
- Quantitative Questions, Quantitative Answers
- 1.2. Causal Effects and Idealized Experiments
- Estimation of Causal Effects
- Prediction, Forecasting, and Causality
- 1.3. Data: Sources and Types
- Experimental versus Observational Data
- Cross-Sectional Data
- Time Series Data
- Panel Data
- 1.1. Economic Questions We Examine
- 2.1. Random Variables and Probability Distributions
- Probabilities, the Sample Space, and Random Variables
- Probability Distribution of a Discrete Random Variable
- Probability Distribution of a Continuous Random Variable
- 2.2. Expected Values, Mean, and Variance
- The Expected Value of a Random Variable
- The Standard Deviation and Variance
- Mean and Variance of a Linear Function of a Random Variable
- Other Measures of the Shape of a Distribution
- Standardized Random Variables
- 2.3. Two Random Variables
- Joint and Marginal Distributions
- Conditional Distributions
- Independence
- Covariance and Correlation
- The Mean and Variance of Sums of Random Variables
- 2.4. The Normal, Chi-Squared, Student t, and F Distributions
- The Normal Distribution
- The Chi-Squared Distribution
- The Student t Distribution
- The F Distribution
- 2.5. Random Sampling and the Distribution of the Sample Average
- Random Sampling
- The Sampling Distribution of the Sample Average
- 2.6. Large-Sample Approximations to Sampling Distributions
- The Law of Large Numbers and Consistency
- The Central Limit Theorem
- Appendix 2.1: Derivation of Results in Key Concept 2.3
- Appendix 2.2: The Conditional Mean as the Minimum Mean Squared Error Predictor
- 3.1. Estimation of the Population Mean
- Estimators and Their Properties
- Properties of Y
- The Importance of Random Sampling
- 3.2. Hypothesis Tests Concerning the Population Mean
- Null and Alternative Hypotheses
- The p-Value
- Calculating the p-Value When sY Is Known
- The Sample Variance, Sample Standard Deviation, and Standard Error
- Calculating the p-Value When sY Is Unknown
- The t-Statistic
- Hypothesis Testing with a Prespecified Significance Level
- One-Sided Alternatives
- 3.3. Confidence Intervals for the Population Mean
- 3.4. Comparing Means from Different Populations
- Hypothesis Tests for the Difference Between Two Means
- Confidence Intervals for the Difference Between Two Population Means
- 3.5. Differences-of-Means Estimation of Causal Effects Using Experimental Data
- The Causal Effect as a Difference of Conditional Expectations
- Estimation of the Causal Effect Using Differences of Means
- 3.6. Using the t-Statistic When the Sample Size Is Small
- The t-Statistic and the Student t Distribution
- Use of the Student t Distribution in Practice
- 3.7. Scatterplots, the Sample Covariance, and the Sample Correlation
- Scatterplots
- Sample Covariance and Correlation
- Appendix 3.1: The U.S. Current Population Survey
- Appendix 3.2: Two Proofs That Y Is the Least Squares Estimator of µY
- Appendix 3.3: A Proof That the Sample Variance Is Consistent
- 4.1. The Linear Regression Model
- 4.2. Estimating the Coefficients of the Linear Regression Model
- The Ordinary Least Squares Estimator
- OLS Estimates of the Relationship Between Test Scores and the Student–Teacher Ratio
- Why Use the OLS Estimator?
- 4.3. Measures of Fit and Prediction Accuracy
- The R2
- The Standard Error of the Regression
- Prediction Using OLS
- Application to the Test Score Data
- 4.4. The Least Squares Assumptions for Causal Inference
- Assumption 1: The Conditional Distribution of ui Given Xi Has a Mean of Zero
- Assumption 2: (Xi, Yi), i = 1,
- Assumption 3: Large Outliers Are Unlikely
- Use of the Least Squares Assumptions
- 4.5. The Sampling Distribution of the OLS Estimators
- 4.6. Conclusion
- Appendix 4.1: The California Test Score Data Set
- Appendix 4.2: Derivation of the OLS Estimators
- Appendix 4.3: Sampling Distribution of the OLS Estimator
- Appendix 4.4: The Least Squares Assumptions for Prediction
- 5.1. Testing Hypotheses About One of the Regression Coefficients
- Two-Sided Hypotheses Concerning ß1
- One-Sided Hypotheses Concerning ß1
- Testing Hypotheses About the Intercept ß0
- 5.2. Confidence Intervals for a Regression Coefficient
- 5.3. Regression When X Is a Binary Variable
- Interpretation of the Regression Coefficients
- 5.4. Heteroskedasticity and Homoskedasticity
- What Are Heteroskedasticity and Homoskedasticity?
- Mathematical Implications of Homoskedasticity
- What Does This Mean in Practice?
- 5.5. The Theoretical Foundations of Ordinary Least Squares
- Linear Conditionally Unbiased Estimators and the Gauss–Markov Theorem
- Regression Estimators Other Than OLS
- 5.6. Using the t-Statistic in Regression When the Sample Size Is Small
- The t-Statistic and the Student t Distribution
- Use of the Student t Distribution in Practice
- 5.7. Conclusion
- Appendix 5.1: Formulas for OLS Standard Errors
- Appendix 5.2: The Gauss–Markov Conditions and a Proof of the Gauss–Markov Theorem
- 6.1. Omitted Variable Bias
- Definition of Omitted Variable Bias
- A Formula for Omitted Variable Bias
- Addressing Omitted Variable Bias by Dividing the Data into Groups
- 6.2. The Multiple Regression Model
- The Population Regression Line
- The Population Multiple Regression Model
- 6.3. The OLS Estimator in Multiple Regression
- The OLS Estimator
- Application to Test Scores and the Student–Teacher Ratio
- 6.4. Measures of Fit in Multiple Regression
- The Standard Error of the Regression (SER)
- The R2
- The Adjusted R2
- Application to Test Scores
- 6.5. The Least Squares Assumptions for Causal Inference in Multiple Regression
- Assumption 1: The Conditional Distribution of ui Given X1i, X2i,
- Assumption 2: (X1i, X2i,
- Assumption 3: Large Outliers Are Unlikely
- Assumption 4: No Perfect Multicollinearity
- 6.6. The Distribution of the OLS Estimators in Multiple Regression
- 6.7. Multicollinearity
- Examples of Perfect Multicollinearity
- Imperfect Multicollinearity
- 6.8. Control Variables and Conditional Mean Independence
- Control Variables and Conditional Mean Independence
- 6.9. Conclusion
- Appendix 6.1: Derivation of Equation (6.1)
- Appendix 6.2: Distribution of the OLS Estimators When There Are Two Regressors and Homoskedastic Err
- Appendix 6.3: The Frisch–Waugh Theorem
- Appendix 6.4: The Least Squares Assumptions for Prediction with Multiple Regressors
- Appendix 6.5: Distribution of OLS Estimators in Multiple Regression with Control Variables
- 7.1. Hypothesis Tests and Confidence Intervals for a Single Coefficient
- Standard Errors for the OLS Estimators
- Hypothesis Tests for a Single Coefficient
- Confidence Intervals for a Single Coefficient
- Application to Test Scores and the Student–Teacher Ratio
- 7.2. Tests of Joint Hypotheses
- Testing Hypotheses on Two or More Coefficients
- The F-Statistic
- Application to Test Scores and the Student–Teacher Ratio
- The Homoskedasticity-Only F-Statistic
- 7.3. Testing Single Restrictions Involving Multiple Coefficients
- 7.4. Confidence Sets for Multiple Coefficients
- 7.5. Model Specification for Multiple Regression
- Model Specification and Choosing Control Variables
- Interpreting the R2 and the Adjusted R2 in Practice
- 7.6. Analysis of the Test Score Data Set
- 7.7. Conclusion
- Appendix 7.1: The Bonferroni Test of a Joint Hypothesis
- 8.1. A General Strategy for Modeling Nonlinear Regression Functions
- Test Scores and District Income
- The Effect on Y of a Change in X in Nonlinear Specifications
- A General Approach to Modeling Nonlinearities Using Multiple Regression
- 8.2. Nonlinear Functions of a Single Independent Variable
- Polynomials
- Logarithms
- Polynomial and Logarithmic Models of Test Scores and District Income
- 8.3. Interactions Between Independent Variables
- Interactions Between Two Binary Variables
- Interactions Between a Continuous and a Binary Variable
- Interactions Between Two Continuous Variables
- 8.4. Nonlinear Effects on Test Scores of the Student–Teacher Ratio
- Discussion of Regression Results
- Summary of Findings
- 8.5. Conclusion
- Appendix 8.1: Regression Functions That Are Nonlinear in the Parameters
- Appendix 8.2: Slopes and Elasticities for Nonlinear Regression Functions
- 9.1. Internal and External Validity
- Threats to Internal Validity
- Threats to External Validity
- 9.2. Threats to Internal Validity of Multiple Regression Analysis
- Omitted Variable Bias
- Misspecification of the Functional Form of the Regression Function
- Measurement Error and Errors-in-Variables Bias
- Missing Data and Sample Selection
- Simultaneous Causality
- Sources of Inconsistency of OLS Standard Errors
- 9.3. Internal and External Validity When the Regression Is Used for Prediction
- 9.4. Example: Test Scores and Class Size
- External Validity
- Internal Validity
- Discussion and Implications
- 9.5. Conclusion
- Appendix 9.1: The Massachusetts Elementary School Testing Data
- 10.1. Panel Data
- Example: Traffic Deaths and Alcohol Taxes
- 10.2. Panel Data with Two Time Periods: “Before and After” Comparisons
- 10.3. Fixed Effects Regression
- The Fixed Effects Regression Model
- Estimation and Inference
- Application to Traffic Deaths
- 10.4. Regression with Time Fixed Effects
- Time Effects Only
- Both Entity and Time Fixed Effects
- 10.5. The Fixed Effects Regression Assumptions and Standard Errors for Fixed Effects Regression
- The Fixed Effects Regression Assumptions
- Standard Errors for Fixed Effects Regression
- 10.6. Drunk Driving Laws and Traffic Deaths
- 10.7. Conclusion
- Appendix 10.1: The State Traffic Fatality Data Set
- Appendix 10.2: Standard Errors for Fixed Effects Regression
- 11.1. Binary Dependent Variables and the Linear Probability Model
- Binary Dependent Variables
- The Linear Probability Model
- 11.2. Probit and Logit Regression
- Probit Regression
- Logit Regression
- Comparing the Linear Probability, Probit, and Logit Models
- 11.3. Estimation and Inference in the Logit and Probit Models
- Nonlinear Least Squares Estimation
- Maximum Likelihood Estimation
- Measures of Fit
- 11.4. Application to the Boston HMDA Data
- 11.5. Conclusion
- Appendix 11.1: The Boston HMDA Data Set
- Appendix 11.2: Maximum Likelihood Estimation
- Appendix 11.3: Other Limited Dependent Variable Models
- 12.1. The IV Estimator with a Single Regressor and a Single Instrument
- The IV Model and Assumptions
- The Two Stage Least Squares Estimator
- Why Does IV Regression Work?
- The Sampling Distribution of the TSLS Estimator
- Application to the Demand for Cigarettes
- 12.2. The General IV Regression Model
- TSLS in the General IV Model
- Instrument Relevance and Exogeneity in the General IV Model
- The IV Regression Assumptions and Sampling Distribution of the TSLS Estimator
- Inference Using the TSLS Estimator
- Application to the Demand for Cigarettes
- 12.3. Checking Instrument Validity
- Assumption 1: Instrument Relevance
- Assumption 2: Instrument Exogeneity
- 12.4. Application to the Demand for Cigarettes
- 12.5. Where Do Valid Instruments Come From?
- Three Examples
- 12.6. Conclusion
- Appendix 12.1: The Cigarette Consumption Panel Data Set
- Appendix 12.2: Derivation of the Formula for the TSLS Estimator in Equation (12.4)
- Appendix 12.3: Large-Sample Distribution of the TSLS Estimator
- Appendix 12.4: Large-Sample Distribution of the TSLS Estimator When the Instrument Is Not Valid
- Appendix 12.5: Instrumental Variables Analysis with Weak Instruments
- Appendix 12.6: TSLS with Control Variables
- 13.1. Potential Outcomes, Causal Effects, and Idealized Experiments
- Potential Outcomes and the Average Causal Effect
- Econometric Methods for Analyzing Experimental Data
- 13.2. Threats to Validity of Experiments
- Threats to Internal Validity
- Threats to External Validity
- 13.3. Experimental Estimates of the Effect of Class Size Reductions
- Experimental Design
- Analysis of the STAR Data
- Comparison of the Observational and Experimental Estimates of Class Size Effects
- 13.4. Quasi-Experiments
- Examples
- The Differences-in-Differences Estimator
- Instrumental Variables Estimators
- Regression Discontinuity Estimators
- 13.5. Potential Problems with Quasi-Experiments
- Threats to Internal Validity
- Threats to External Validity
- 13.6. Experimental and Quasi-Experimental Estimates in Heterogeneous Populations
- OLS with Heterogeneous Causal Effects
- IV Regression with Heterogeneous Causal Effects
- 13.7. Conclusion
- Appendix 13.1: The Project STAR Data Set
- Appendix 13.2: IV Estimation When the Causal Effect Varies Across Individuals
- Appendix 13.3: The Potential Outcomes Framework for Analyzing Data from Experiments
- 14.1. What Is “Big Data”?
- 14.2. The Many-Predictor Problem and OLS
- The Mean Squared Prediction Error
- The First Least Squares Assumption for Prediction
- The Predictive Regression Model with Standardized Regressors
- The MSPE of OLS and the Principle of Shrinkage
- Estimation of the MSPE
- 14.3. Ridge Regression
- Shrinkage via Penalization and Ridge Regression
- Estimation of the Ridge Shrinkage Parameter by Cross Validation
- Application to School Test Scores
- 14.4. The Lasso
- Shrinkage Using the Lasso
- Application to School Test Scores
- 14.5. Principal Components
- Principals Components with Two Variables
- Principal Components with k Variables
- Application to School Test Scores
- 14.6. Predicting School Test Scores with Many Predictors
- 14.7. Conclusion
- Appendix 14.1: The California School Test Score Data Set
- Appendix 14.2: Derivation of Equation (14.4) for k = 1
- Appendix 14.3: The Ridge Regression Estimator When k = 1
- Appendix 14.4: The Lasso Estimator When k = 1
- Appendix 14.5: Computing Out-of-Sample Predictions in the Standardized Regression Model
- 15.1. Introduction to Time Series Data and Serial Correlation
- Real GDP in the United States
- Lags, First Differences, Logarithms, and Growth Rates
- Autocorrelation
- Other Examples of Economic Time Series
- 15.2. Stationarity and the Mean Squared Forecast Error
- Stationarity
- Forecasts and Forecast Errors
- The Mean Squared Forecast Error
- 15.3. Autoregressions
- The First-Order Autoregressive Model
- The pth-Order Autoregressive Model
- 15.4. Time Series Regression with Additional Predictors and the Autoregressive Distributed Lag Model
- Forecasting GDP Growth Using the Term Spread
- The Autoregressive Distributed Lag Model
- The Least Squares Assumptions for Forecasting with Multiple Predictors
- 15.5. Estimation of the MSFE and Forecast Intervals
- Estimation of the MSFE
- Forecast Uncertainty and Forecast Intervals
- 15.6. Estimating the Lag Length Using Information Criteria
- Determining the Order of an Autoregression
- Lag Length Selection in Time Series Regression with Multiple Predictors
- 15.7. Nonstationarity I: Trends
- What Is a Trend?
- Problems Caused by Stochastic Trends
- Detecting Stochastic Trends: Testing for a Unit AR Root
- Avoiding the Problems Caused by Stochastic Trends
- 15.8. Nonstationarity II: Breaks
- What Is a Break?
- Testing for Breaks
- Detecting Breaks Using Pseudo Out-of-Sample Forecasts
- Avoiding the Problems Caused by Breaks
- 15.9. Conclusion
- Appendix 15.1: Time Series Data Used in Chapter 15
- Appendix 15.2: Stationarity in the AR(1) Model
- Appendix 15.3: Lag Operator Notation
- Appendix 15.4: ARMA Models
- Appendix 15.5: Consistency of the BIC Lag Length Estimator
- 16.1. An Initial Taste of the Orange Juice Data
- 16.2. Dynamic Causal Effects
- Causal Effects and Time Series Data
- Two Types of Exogeneity
- 16.3. Estimation of Dynamic Causal Effects with Exogenous Regressors
- The Distributed Lag Model Assumptions
- Autocorrelated ut, Standard Errors, and Inference
- Dynamic Multipliers and Cumulative Dynamic Multipliers
- 16.4. Heteroskedasticity- and Autocorrelation-Consistent Standard Errors
- Distribution of the OLS Estimator with Autocorrelated Errors
- HAC Standard Errors
- 16.5. Estimation of Dynamic Causal Effects with Strictly Exogenous Regressors
- The Distributed Lag Model with AR(1) Errors
- OLS Estimation of the ADL Model
- GLS Estimation
- 16.6. Orange Juice Prices and Cold Weather
- 16.7. Is Exogeneity Plausible? Some Examples
- U.S. Income and Australian Exports
- Oil Prices and Inflation
- Monetary Policy and Inflation
- The Growth Rate of GDP and the Term Spread
- 16.8. Conclusion
- Appendix 16.1: The Orange Juice Data Set
- Appendix 16.2: The ADL Model and Generalized Least Squares in Lag Operator Notation
- 17.1. Vector Autoregressions
- The VAR Model
- A VAR Model of the Growth Rate of GDP and the Term Spread
- 17.2. Multi-period Forecasts
- Iterated Multi-period Forecasts
- Direct Multi-period Forecasts
- Which Method Should You Use?
- 17.3. Orders of Integration and the Nonnormality of Unit Root Test Statistics
- Other Models of Trends and Orders of Integration
- Why Do Unit Root Tests Have Nonnormal Distributions?
- 17.4. Cointegration
- Cointegration and Error Correction
- How Can You Tell Whether Two Variables Are Cointegrated?
- Estimation of Cointegrating Coefficients
- Extension to Multiple Cointegrated Variables
- 17.5. Volatility Clustering and Autoregressive Conditional Heteroskedasticity
- Volatility Clustering
- Realized Volatility
- Autoregressive Conditional Heteroskedasticity
- Application to Stock Price Volatility
- 17.6. Forecasting with Many Predictors Using Dynamic Factor Models and Principal Components
- The Dynamic Factor Model
- The DFM: Estimation and Forecasting
- Application to U.S. Macroeconomic Data
- 17.7. Conclusion
- Appendix 17.1: The Quarterly U.S. Macro Data Set
- 18.1. The Extended Least Squares Assumptions and the OLS Estimator
- The Extended Least Squares Assumptions
- The OLS Estimator
- 18.2. Fundamentals of Asymptotic Distribution Theory
- Convergence in Probability and the Law of Large Numbers
- The Central Limit Theorem and Convergence in Distribution
- Slutsky’s Theorem and the Continuous Mapping Theorem
- Application to the t-Statistic Based on the Sample Mean
- 18.3. Asymptotic Distribution of the OLS Estimator and t-Statistic
- Consistency and Asymptotic Normality of the OLS Estimators
- Consistency of Heteroskedasticity-Robust Standard Errors
- Asymptotic Normality of the Heteroskedasticity-Robust t-Statistic
- 18.4. Exact Sampling Distributions When the Errors Are Normally Distributed
- Distribution of b n 1 with Normal Errors
- Distribution of the Homoskedasticity-Only t-Statistic
- 18.5. Weighted Least Squares
- WLS with Known Heteroskedasticity
- WLS with Heteroskedasticity of Known Functional Form
- Heteroskedasticity-Robust Standard Errors or WLS?
- Appendix 18.1: The Normal and Related Distributions and Moments of Continuous Random Variables
- Appendix 18.2: Two Inequalities
- 19.1. The Linear Multiple Regression Model and OLS Estimator in Matrix Form
- The Multiple Regression Model in Matrix Notation
- The Extended Least Squares Assumptions
- The OLS Estimator
- 19.2. Asymptotic Distribution of the OLS Estimator and t-Statistic
- The Multivariate Central Limit Theorem
- Asymptotic Normality of b n
- Heteroskedasticity-Robust Standard Errors
- Confidence Intervals for Predicted Effects
- Asymptotic Distribution of the t-Statistic
- 19.3. Tests of Joint Hypotheses
- Joint Hypotheses in Matrix Notation
- Asymptotic Distribution of the F-Statistic
- Confidence Sets for Multiple Coefficients
- 19.4. Distribution of Regression Statistics with Normal Errors
- Matrix Representations of OLS Regression Statistics
- Distribution of b n with Independent Normal Errors
- Distribution of su 2 N
- Homoskedasticity-Only Standard Errors
- Distribution of the t-Statistic
- Distribution of the F-Statistic
- 19.5. Efficiency of the OLS Estimator with Homoskedastic Errors
- The Gauss–Markov Conditions for Multiple Regression
- Linear Conditionally Unbiased Estimators
- The Gauss–Markov Theorem for Multiple Regression
- 19.6. Generalized Least Squares
- The GLS Assumptions
- GLS When O Is Known
- GLS When O Contains Unknown Parameters
- The Conditional Mean Zero Assumption and GLS
- 19.7. Instrumental Variables and Generalized Method of Moments Estimation
- The IV Estimator in Matrix Form
- Asymptotic Distribution of the TSLS Estimator
- Properties of TSLS When the Errors Are Homoskedastic
- Generalized Method of Moments Estimation in Linear Models
- Appendix 19.1: Summary of Matrix Algebra
- Appendix 19.2: Multivariate Distributions
- Appendix 19.3: Derivation of the Asymptotic Distribution of b n
- Appendix 19.4: Derivations of Exact Distributions of OLS Test Statistics with Normal Errors
- Appendix 19.5: Proof of the Gauss–Markov Theorem for Multiple Regression
- Appendix 19.6: Proof of Selected Results for IV and GMM Estimation
- Appendix 19.7: Regression with Many Predictors: MSPE, Ridge Regression, and Principal Components Ana
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