Introductory Econometrics: A Modern Approach
Practical and professional, Wooldridge’s INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e bridges the gap between how undergraduate econometrics has traditionally been taught and how empirical researchers actually think about and apply econometric methods. The text’s unique approach reflects how econometric instruction has evolved from simply describing a set of abstract recipes to showing how econometrics can be used to empirically study questions across a variety of disciplines. The systematic approach, where assumptions are introduced only as they are needed to obtain a certain result, makes the material easier for students, and leads to better econometric practice. Unlike traditional texts, INTRODUCTORY ECONOMETRICS is organized around the type of data being analyzed -- an approach that simplifies the exposition and allows a more careful discussion of assumptions. Packed with relevant applications and a wealth of interesting data sets, the text emphasizes examples that have implications for policy or provide evidence for or against economic theories.
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2SLS assume asymptotic average bias binary ceteris paribus Chapter coefficient confidence interval consistent estimator critical value cross-sectional data set denote dependent differencing dummy variable econometric economic endogenous equation error term example exogenous expected value exper Explain explanatory variables F statistic factors fitted values fixed effects forecast function Gauss-Markov assumptions heteroskedasticity heteroskedasticity-robust homoskedasticity income increase independent variables instrumental variables intercept interpret least squares linear model log(wage matrix mean multiple regression normal distribution null hypothesis observations obtain OLS estimators p-value panel data parameters partial effect percentage population predicted probability probit problem R-squared random sample random variable regression analysis regression model reject H0 return to education Section serial correlation significance level simple regression slope squared residuals standard errors standard normal statistically significant sum of squared Suppose Theorem tion Tobit model trend unbiased estimator uncorrelated unit root unobserved effect variance wage zero