Book , Print in English

Introduction to econometrics

James H. Stock, Mark W. Watson.
  • Boston : Addison-Wesley, ©2011.
  • 3rd ed.
  • xlii, 785 pages : illustrations; 24 cm.
Subjects
Series
Contents
  • pt. ONE Introduction and Review
  • ch. 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 How Much Do Cigarette Taxes Reduce Smoking?
  • Question #4 What Will the Rate of Inflation Be Next Year?
  • Quantitative Questions, Quantitative Answers
  • 1.2. Causal Effects and Idealized Experiments
  • Estimation of Causal Effects
  • Forecasting and Causality
  • 1.3. Data: Sources and Types
  • Experimental Versus Observational Data
  • Cross-Sectional Data
  • Time Series Data
  • Panel Data
  • ch. 2 Review of Probability
  • 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
  • Expected Value of a Random Variable
  • Standard Deviation and Variance
  • Mean and Variance of a Linear Function of a Random Variable
  • Other Measures of the Shape of a Distribution
  • 2.3. Two Random Variables
  • Joint and Marginal Distributions
  • Conditional Distributions
  • Independence
  • Covariance and Correlation
  • Mean and Variance of Sums of Random Variables
  • 2.4. Normal, Chi-Squared, Student t, and F Distributions
  • Normal Distribution
  • Chi-Squared Distribution
  • Student t Distribution
  • F Distribution
  • 2.5. Random Sampling and the Distribution of the Sample Average
  • Random Sampling
  • Sampling Distribution of the Sample Average
  • 2.6. Large-Sample Approximations to Sampling Distributions
  • Law of Large Numbers and Consistency
  • Central Limit Theorem
  • Appendix 2.1 Derivation of Results in Key Concept 2.3
  • ch. 3 Review of Statistics
  • 3.1. Estimation of the Population Mean
  • Estimators and Their Properties
  • Properties of Y
  • Importance of Random Sampling
  • 3.2. Hypothesis Tests Concerning the Population Mean
  • Null and Alternative Hypotheses
  • p-Value
  • Calculating the p-Value When σy Is Known
  • Sample Variance, Sample Standard Deviation, and Standard Error
  • Calculating the p-Value When σy Is Unknown
  • 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
  • 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
  • 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 U.S. Current Population Survey
  • Appendix 3.2 Two Proofs That Y Is the Least Squares Estimator of μY
  • Appendix 3.3 Proof That the Sample Variance Is Consistent
  • pt. TWO Fundamentals of Regression Analysis
  • ch. 4 Linear Regression with One Regressor
  • 4.1. Linear Regression Model
  • 4.2. Estimating the Coefficients of the Linear Regression Model
  • 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
  • R2
  • Standard Error of the Regression
  • Application to the Test Score Data
  • 4.4. Least Squares Assumptions
  • Assumption #1 Conditional Distribution of Ui Given Xi Has a Mean of Zero
  • Assumption #2 (Xi, Yi), i = 1,.., n, Are Independently and Identically Distributed
  • Assumption #3 Large Outliers Are Unlikely
  • Use of the Least Squares Assumptions
  • 4.5. Sampling Distribution of the OLS Estimators
  • Sampling Distribution of the OLS Estimators
  • 4.6. Conclusion
  • Appendix 4.1 California Test Score Data Set
  • Appendix 4.2 Derivation of the OLS Estimators
  • Appendix 4.3 Sampling Distribution of the OLS Estimator
  • ch. 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
  • 5.1. Testing Hypotheses About One of the Regression Coefficients
  • Two-Sided Hypotheses Concerning β1
  • One-Sided Hypotheses Concerning β1
  • Testing Hypotheses About the Intercept β0
  • 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. 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
  • 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 Gauss-Markov Conditions and a Proof of the Gauss-Markov Theorem
  • ch. 6 Linear Regression with Multiple Regressors
  • 6.1. Omitted Variable Bias
  • Definition of Omitted Variable Bias
  • Formula for Omitted Variable Bias
  • Addressing Omitted Variable Bias by Dividing the Data into Groups
  • 6.2. Multiple Regression Model
  • Population Regression Line
  • Population Multiple Regression Model
  • 6.3. OLS Estimator in Multiple Regression
  • OLS Estimator
  • Application to Test Scores and the Student-Teacher Ratio
  • 6.4. Measures of Fit in Multiple Regression
  • Standard Error of the Regression (SER)
  • R2
  • "Adjusted R2"
  • Application to Test Scores
  • 6.5. Least Squares Assumptions in Multiple Regression
  • Assumption #1 Conditional Distribution of ui Given X1i, X2i,..., Xki Has a Mean of Zero
  • Assumption #2 (X1i, X2i, ..., Xki, Yi), i=1,..., n, Are i.i.d.
  • Assumption #3 Large Outliers Are Unlikely
  • Assumption #4 No Perfect Multicollinearity
  • 6.6. Distribution of the OLS Estimators in Multiple Regression
  • 6.7. Multicollinearity
  • Examples of Perfect Multicollinearity
  • Imperfect Multicollinearity
  • 6.8. Conclusion
  • Appendix 6.1 Derivation of Equation (6.1)
  • Appendix 6.2 Distribution of the OLS Estimators When There Are Two Regressors and Homoskedastic Errors
  • Appendix 6.3 Frisch-Waugh Theorem
  • ch. 7 Hypothesis Tests and Confidence Intervals in Multiple Regression
  • 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
  • F-Statistic
  • Application to Test Scores and the Student-Teacher Ratio
  • 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
  • Omitted Variable Bias in Multiple Regression
  • Role of Control Variables in Multiple Regression
  • Model Specification in Theory and in Practice
  • Interpreting the R2 and the Adjusted R2 in Practice
  • 7.6. Analysis of the Test Score Data Set
  • 7.7. Conclusion
  • Appendix 7.1 Bonferroni Test of a Joint Hypothesis
  • Appendix 7.2 Conditional Mean Independence
  • ch. 8 Nonlinear Regression Functions
  • 8.1. General Strategy for Modeling Nonlinear Regression Functions
  • Test Scores and District Income
  • Effect on Y of a Change in X in Nonlinear Specifications
  • 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
  • ch. 9 Assessing Studies Based on Multiple Regression
  • 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 Forecasting
  • Using Regression Models for Forecasting
  • Assessing the Validity of Regression Models for Forecasting
  • 9.4. Example: Test Scores and Class Size
  • External Validity --
  • Contents note continued: Internal Validity
  • Discussion and Implications
  • 9.5. Conclusion
  • Appendix 9.1 Massachusetts Elementary School Testing Data
  • pt. THREE Further Topics in Regression Analysis
  • ch. 10 Regression with Panel 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
  • 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. Fixed Effects Regression Assumptions and Standard Errors for Fixed Effects Regression
  • Fixed Effects Regression Assumptions
  • Standard Errors for Fixed Effects Regression
  • 10.6. Drunk Driving Laws and Traffic Deaths
  • 10.7. Conclusion
  • Appendix 10.1 State Traffic Fatality Data Set
  • Appendix 10.2 Standard Errors for Fixed Effects Regression
  • ch. 11 Regression with a Binary Dependent Variable
  • 11.1. Binary Dependent Variables and the Linear Probability Model
  • Binary Dependent Variables
  • 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 Boston HMDA Data Set
  • Appendix 11.2 Maximum Likelihood Estimation
  • Appendix 11.3 Other Limited Dependent Variable Models
  • ch. 12 Instrumental Variables Regression
  • 12.1. IV Estimator with a Single Regressor and a Single Instrument
  • IV Model and Assumptions
  • Two Stage Least Squares Estimator
  • Why Does IV Regression Work?
  • Sampling Distribution of the TSLS Estimator
  • Application to the Demand for Cigarettes
  • 12.2. General IV Regression Model
  • TSLS in the General IV Model
  • Instrument Relevance and Exogeneity in the General IV Model
  • 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 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
  • ch. 13 Experiments and Quasi-Experiments
  • 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
  • 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 Project STAR Data Set
  • Appendix 13.2 IV Estimation When the Causal Effect Varies Across Individuals
  • Appendix 13.3 Potential Outcomes Framework for Analyzing Data from Experiments
  • pt. FOUR Regression Analysis of Economic Time Series Data
  • ch. 14 Introduction to Time Series Regression and Forecasting
  • 14.1. Using Regression Models for Forecasting
  • 14.2. Introduction to Time Series Data and Serial Correlation
  • Rates of Inflation and Unemployment in the United States
  • Lags, First Differences, Logarithms, and Growth Rates
  • Autocorrelation
  • Other Examples of Economic Time Series
  • 14.3. Autoregressions
  • First Order Autoregressive Model
  • pth Order Autoregressive Model
  • 14.4. Time Series Regression with Additional Predictors and the Autoregressive Distributed Lag Model
  • Forecasting Changes in the Inflation Rate Using Past Unemployment Rates
  • Stationarity
  • Time Series Regression with Multiple Predictors
  • Forecast Uncertainty and Forecast Intervals
  • 14.5. Lag Length Selection Using Information Criteria
  • Determining the Order of an Autoregression
  • Lag Length Selection in Time Series Regression with Multiple Predictors
  • 14.6. 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
  • 14.7. Nonstationarity II: Breaks
  • What Is a Break?
  • Testing for Breaks
  • Pseudo Out-of-Sample Forecasting
  • Avoiding the Problems Caused by Breaks
  • 14.8. Conclusion
  • Appendix 14.1 Time Series Data Used in Chapter 14
  • Appendix 14.2 Stationarity in the AR(1) Model
  • Appendix 14.3 Lag Operator Notation
  • Appendix 14.4 ARMA Models
  • Appendix 14.5 Consistency of the BIC Lag Length Estimator
  • ch. 15 Estimation of Dynamic Causal Effects
  • 15.1. Initial Taste of the Orange Juice Data
  • 15.2. Dynamic Causal Effects
  • Causal Effects and Time Series Data
  • Two Types of Exogeneity
  • 15.3. Estimation of Dynamic Causal Effects with Exogenous Regressors
  • Distributed Lag Model Assumptions
  • Autocorrelated U Standard Errors, and Inference
  • Dynamic Multipliers and Cumulative Dynamic Multipliers
  • 15.4. Heteroskedasticity- and Autocorrelation-Consistent Standard Errors
  • Distribution of the OLS Estimator with Autocorrelated Errors
  • HAC Standard Errors
  • 15.5. Estimation of Dynamic Causal Effects with Strictly Exogenous Regressors
  • Distributed Lag Model with AR(1) Errors
  • OLS Estimation of the ADL Model
  • GLS Estimation
  • Distributed Lag Model with Additional Lags and AR(p) Errors
  • 15.6. Orange Juice Prices and Cold Weather
  • 15.7. Is Exogeneity Plausible? Some Examples
  • U.S. Income and Australian Exports
  • Oil Prices and Inflation
  • Monetary Policy and Inflation
  • Phillips Curve
  • 15.8. Conclusion
  • Appendix 15.1 Orange Juice Data Set
  • Appendix 15.2 ADL Model and Generalized Least Squares in Lag Operator Notation
  • ch. 16 Additional Topics in Time Series Regression
  • 16.1. Vector Autoregressions
  • VAR Model
  • VAR Model of the Rates of Inflation and Unemployment
  • 16.2. Multiperiod Forecasts
  • Iterated Multiperiod Forecasts
  • Direct Multiperiod Forecasts
  • Which Method Should You Use?
  • 16.3. Orders of Integration and the DF-GLS Unit Root Test
  • Other Models of Trends and Orders of Integration
  • DF-GLS Test for a Unit Root
  • Why Do Unit Root Tests Have Nonnormal Distributions?
  • 16.4. Cointegration
  • Cointegration and Error Correction
  • How Can You Tell Whether Two Variables Are Cointegrated?
  • Estimation of Cointegrating Coefficients
  • Extension to Multiple Cointegrated Variables
  • Application to Interest Rates
  • 16.5. Volatility Clustering and Autoregressive Conditional Heteroskedasticity
  • Volatility Clustering
  • Autoregressive Conditional Heteroskedasticity
  • Application to Stock Price Volatility
  • 16.6. Conclusion
  • Appendix 16.1 U.S. Financial Data Used in Chapter 16
  • pt. FIVE Econometric Theory of Regression Analysis
  • ch. 17 Theory of Linear Regression with One Regressor
  • 17.1. Extended Least Squares Assumptions and the OLS Estimator
  • Extended Least Squares Assumptions
  • OLS Estimator
  • 17.2. Fundamentals of Asymptotic Distribution Theory
  • Convergence in Probability and the Law of Large Numbers
  • 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
  • 17.3. 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
  • 17.4. Exact Sampling Distributions When the Errors Are Normally Distributed
  • Distribution of β1 with Normal Errors
  • Distribution of the Homoskedasticity-Only t-Statistic
  • 17.5. Weighted Least Squares
  • WLS with Known Heteroskedasticity
  • WLS with Heteroskedasticity of Known Functional Form
  • Heteroskedasticity-Robust Standard Errors or WLS?
  • Appendix 17.1 Normal and Related Distributions and Moments of Continuous Random Variables
  • Appendix 17.2 Two Inequalities
  • ch. 18 Theory of Multiple Regression
  • 18.1. Linear Multiple Regression Model and OLS Estimator in Matrix Form
  • Multiple Regression Model in Matrix Notation
  • Extended Least Squares Assumptions
  • OLS Estimator
  • 18.2. Asymptotic Distribution of the OLS Estimator and t-Statistic --
  • Contents note continued: Multivariate Central Limit Theorem
  • Asymptotic Normality of β
  • Heteroskedasticity-Robust Standard Errors
  • Confidence Intervals for Predicted Effects
  • Asymptotic Distribution of the t-Statistic
  • 18.3. Tests of Joint Hypotheses
  • Joint Hypotheses in Matrix Notation
  • Asymptotic Distribution of the F-Statistic
  • Confidence Sets for Multiple Coefficients
  • 18.4. Distribution of Regression Statistics with Normal Errors
  • Matrix Representations of OLS Regression Statistics
  • Distribution of β for Normal Errors
  • Distribution of S2
  • Homoskedasticity-Only Standard Errors
  • Distribution of the t-Statistic
  • Distribution of the F-Statistic
  • 18.5. Efficiency of the OLS Estimator with Homoskedastic Errors
  • Gauss-Markov Conditions for Multiple Regression
  • Linear Conditionally Unbiased Estimators
  • Gauss-Markov Theorem for Multiple Regression
  • 18.6. Generalized Least Squares
  • GLS Assumptions
  • GLS When Ω Is Known
  • GLS When Ω Contains Unknown Parameters
  • Zero Conditional Mean Assumption and GLS
  • 18.7. Instrumental Variables and Generalized Method of Moments Estimation
  • 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 18.1 Summary of Matrix Algebra
  • Appendix 18.2 Multivariate Distributions
  • Appendix 18.3 Derivation of the Asymptotic Distribution of β
  • Appendix 18.4 Derivations of Exact Distributions of OLS Test Statistics with Normal Errors
  • Appendix 18.5 Proof of the Gauss-Markov Theorem for Multiple Regression
  • Appendix 18.6 Proof of Selected Results for IV and GMM Estimation.
Other information
  • Includes bibliographical references (p. 757-761) and index.
ISBN
  • 9780138009007 (alk. paper)
  • 0138009007 (alk. paper)
  • 9781408264331 (pbk.)
  • 1408264331 (pbk.)
Identifying numbers
  • LCCN: 2010042379
  • OCLC: 668404425
  • OCLC: 668404425