Introduction to EconometricsA thorough understanding of econometrics allows students to better understand the relationships on which people, businesses, and governments base their decisions. And to make econometrics relevant in an introductory course, interesting applications must motivate the theory and the theory must match the applications. This text motivates the need for tools with concrete applications, and then provide simple assumptions that match the application. Because the theory is immediately relevant to the applications, this approach makes econometrics come alive. Real-world questions and data are integrated into the theoretical development, and appropriate coverage is given to the substantive findings of the resulting empirical analysis. And topics presented in the text reflect modern theory and practice. |
Contents
Introduction and Review | 1 |
Economic Questions and Data | 3 |
CHAPTER | 4 |
Copyright | |
51 other sections not shown
Other editions - View all
Introduction to Econometrics, Student Value Edition James H. Stock,Mark W. Watson No preview available - 2018 |
Introduction to Econometrics, Global Edition James H. Stock,Mark W. Watson No preview available - 2019 |
Introduction to Econometrics, Global Edition James H. Stock,Mark W. Watson No preview available - 2019 |
Common terms and phrases
Appendix applications approximately autoregressive B₁ binary variable central limit theorem Chapter cigarette computed confidence interval correlation covariance crashes data set denoted dependent variable distributed lag earnings econometrics economic effect on test English learners error term esti example exogenous expected value Figure forecast formula heteroskedasticity homoskedastic hypothesis test inflation instrumental variables intercept Key Concept large numbers large samples least squares assumptions logarithm multiple regression model nonlinear nonlinear regression normal distribution null hypothesis observations OLS estimator omitted variable bias p-value panel data percentage of English population mean population regression function population regression line Pr(Y probability distribution probit random sampling random variable randomly regres regressors sample average sampling distribution Section series data significance level single regressor slope specification ẞ₁ standard deviation standard errors standard normal statistical student-teacher ratio summarized in Key t-statistic Table test scores tion treatment unbiased variance X₁ Y₁ zero