Book
,
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Introduction to econometrics
James H. Stock, Mark W. Watson.
- Boston : Addison-Wesley, ©2011.
- 3rd ed.
- xlii, 785 pages : illustrations; 24 cm.
-
Eisenhower B LevelAvailable
-
Washington LRC ReservesAvailable
- 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