04110cam a2200325 a 4500 115836 20230410105342.0 061124s2008 maua b 001 0 eng 14645018 2006039352 0321432517 (alk. paper) 9780321432513 0536782555 9780536782557 DLC DLC DLC HB 139 S864i 2008 330.015195 Stock, James H. Introduction to econometrics / James H. Stock, Mark W. Watson. Boston : Pearson/Addison Wesley, 2008. xiii, 379 p. : ill. ; 24 cm. Includes bibliographical references (p. 359) and index. PART ONE INTRODUCTION AND REVIEW Chapter 1 Economic Questions and Data 1.1 Economic Questions We Examine 1.2 Causal Effects and Idealized Experiments 1.3 Data: Sources and Types Chapter 2 Review of Probability 2.1 Random Variables and Probability Distributions 2.2 Expected Values, Mean, and Variance 2.3 Two Random Variables 2.4 The Normal, Chi-Squared, Studentt, and F Distributions 2.5 Random Sampling and the Distribution of the Sample Average 2.6 Large-Sample Approximations to Sampling Distributions Chapter 3 Review of Statistics 3.1 Estimation of the Population Mean 3.2 Hypothesis Tests Concerning the Population Mean 3.3 Confidence Intervals for the Population Mean 3.4 Comparing Means from Different Populations 3.5 Differences-of-Means Estimation of Causal Effects 3.6 Using the t-Statistic When the Sample Size Is Small 3.7 Scatterplot, the Sample Covariance, and the Sample Correlation Using Experimental Data 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 4.3 Measures of Fit 4.5 The Sampling Distribution of the OLS Estimators 4.6 Conclusion Chapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals 5.1 Testing Hypotheses About One of the Regression Coefficients 5.2 Confidence Intervals for a Regression Coefficient 5.3 Regression When X Is a Binary Variable 5.5 The Theoretical Foundations of Ordinary Least Squares 5.5 The Theoretical Foundations of Ordinary Least Squares 5.6 Using the t-Statistic in Regression When the Sample Size Is Small 5.7 Conclusion Chapter 6 Linear Regression with Multiple Regressors 6.1 Omitted Variable Bias 6.2 The Multiple Regression Model 6.3 The OLS Estimator in Multiple Regression 6.4 Measures of Fit in Multiple Regression 6.5 The Least Squares Assumptions in Multiple Regression 6.6 The Distribution of the OLS Estimators 6.7 Multicollinearity 6.8 Conclusion Chapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression 7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient 7.2 Tests of Joint Hypotheses 7.3 Testing Single Restrictions Involving Multiple Coefficients 7.4 Confidence Sets for Multiple Coefficients 7.6 Analysis of the Test Score Data Set 7.7 Conclusion Chapter 8 Nonlinear Regression Functions 8.1 A General Strategy for Modeling Nonlinear Regression Functions 8.2 Nonlinear Functions of a Single Independent Variable 8.4 Nonlinear Effects on Test Scores of the Student-Teacher Ratio 8.5 Conclusion Chapter 9 Assessing Studies Based on Multiple Regression 9.1 Internal and External Validity 9.2 Threats to Internal Validity of Multiple Regression Analysis 9.3 Internal and External Validity When the Regression Is Used for Forecasting 9.4 Example: Test Scores and Class Size 9.5 Conclusion Chapter 10 Conducting a Regression Study Using Economic Data 10.1 Choosing a Topic 10.2 Collecting the Data 10.3 Conducting Your Regression Analysis 10.4 Writing Up Your Results Appendix References Answers to "Review the Concepts" Questions Glossary Index Econometrics. Econometría. Primera Jornada de Catalogacion. Watson, Mark W. Table of contents only http://www.loc.gov/catdir/toc/ecip075/2006039352.html lcc bk 34968 34968 0 0 lcc 0 0 CS BJB BJB CS 2023-04-10 1 0 HB 139 S864i 2008 00000109712 2018-10-24 00:00:00 1 2018-10-24 bk