Introduction to econometrics / / James H. Stock, Mark W. Watson

• Autore: Stock James H.
• Pubblicazione: Boston : , : Pearson, , 2015
• Edizione: Updated Third, Global edition.
• Descrizione fisica: 1 online resource (841 pages) : illustrations, tables
• Disciplina: 330.015195
• Soggetto topico: Econometrics
• Persona (resp. second.): WatsonMark W.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Preface -- PART ONE Introduction and Review -- 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: How Much Do Cigarette Taxes Reduce Smoking? -- 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 -- Forecasting and Causality -- 1.3 Data: Sources and Types -- Experimental Versus Observational Data -- Cross-Sectional Data -- Time Series Data -- Panel Data -- CHAPTER 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 -- 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 -- 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 -- CHAPTER 3 Review of Statistics -- 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 σγ 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 ?Y -- APPENDIX 3.3 A Proof That the Sample Variance Is Consistent -- PART TWO Fundamentals of Regression Analysis -- CHAPTER 4 Linear Regression with One Regressor -- 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. ; The R2 -- The Standard Error of the Regression -- Application to the Test Score Data -- 4.4 The Least Squares Assumptions -- Assumption #1: The 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 -- 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 -- CHAPTER 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 β -- 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 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 -- CHAPTER 6 Linear Regression with Multiple Regressors -- 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 in Multiple Regression -- Assumption #1: The Conditional Distribution of ui Given X1i, X2i, c, Xki Has a Mean of Zero -- Assumption #2: (X1i, X2i, c, Xki, Yi), i = 1, c, n, Are i.i.d. -- 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 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 The Frisch-Waugh Theorem -- CHAPTER 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 -- 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 -- Omitted Variable Bias in Multiple Regression -- The 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 The Bonferroni Test of a Joint Hypothesis -- APPENDIX 7.2 Conditional Mean Independence -- CHAPTER 8 Nonlinear Regression Functions -- 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 -- CHAPTER 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.
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• Titolo autorizzato: Introduction to econometrics
• ISBN: 1-292-07136-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910154763703321
• Lo trovi qui: Univ. Federico II
• Serie: Pearson series in economics.