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Record Nr. |
UNINA9910154763703321 |
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Autore |
Stock James H. |
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Titolo |
Introduction to econometrics / / James H. Stock, Mark W. Watson |
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Pubbl/distr/stampa |
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Boston : , : Pearson, , 2015 |
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ISBN |
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Edizione |
[Updated Third, Global edition.] |
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Descrizione fisica |
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1 online resource (841 pages) : illustrations, tables |
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Collana |
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The Pearson Series in Economics |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 -- |
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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- |
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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. |
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Sources of Inconsistency of OLS Standard Errors. |
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Sommario/riassunto |
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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 Third Edition Update maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. This program provides a better teaching and learning experience-for you and your students. Here's how: Keeping it current with new and updated discussions on topics of particular interest to today's students. Presenting consistency through theory that matches application. Offering a full array of pedagogical features. MyEconLab® is not included. Students, if MyEconLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. MyEconLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. MyEconLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts. |
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