Introductory Econometrics: A Modern ApproachPractical 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. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. 
What people are saying  Write a review
User ratings
5 stars 
 
4 stars 
 
3 stars 
 
2 stars 
 
1 star 

Review: Introductory Econometrics: A Modern Approach
User Review  Lauren Skora  GoodreadsI think after all the 'metrics classes I did actually read 99% of it. Read full review
Review: Introductory Econometrics: A Modern Approach
User Review  Daniel Babiak  GoodreadsAbsolute best place to start with metrics. GET THE DATASETS AND WORK THROUGH EVERY. EXAMPLE. IN. THE. BOOK. Read full review
Contents
The Nature of Econometrics and Economic Data  1 
Regression Analysis with CrossSectional Data  21 
Regression Analysis with Time Series Data  339 
Advanced Topics  443 
Basic Mathematical Tools  695 
Fundamentals of Probability  714 
Fundamentals of Mathematical Statistics  747 
Summary of Matrix Algebra  788 
The Linear Regression Model in Matrix Form  799 
Answers to Chapter Questions  813 
Statistical Tables  823 
830  
Glossary  835 
849  
Other editions  View all
Common terms and phrases
2SLS assume asymptotic average bias binary ceteris paribus Chapter coefficient confidence interval consistent estimator critical value crosssectional 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 GaussMarkov assumptions heteroskedasticity heteroskedasticityrobust 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 pvalue panel data parameters partial effect percentage population predicted probability probit problem Rsquared 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