Introduction to Econometrics, 4/e (Paperback)

本書序言

Prepare students to work with modern applications and very large data sets, including applications that predict consumer choices and work with nonstandard data (e.g., text data).
●A new Chapter 14 is dedicated to big data and machine learning methods. In economics, many applications focus on the “many-predictor” problem, where the number of predictors is large relative to the sample size. This chapter introduces students to methods beyond the ordinary least squares method that can help them have much lower out-of-sample prediction errors.
●Chapter 17 extends the many-predictor focus of Chapter 14 to time series data. Using the dynamic factor model and a 131-variable set of US quarterly macroeconomic data, students learn how to forecast future values — an important skill to have as professionals in the field of econometrics.
●Regression is now introduced with a parallel treatment of prediction and causal inference, to expose students to the different demands on how data can be collected (i.e., randomized vs. controlled variables).
Keep students engaged with a full array of pedagogical material, tools, and resources
●General Interest boxes provide students with interesting insight into related topics, while also highlighting real-world studies. The 4th Edition now extends discussion of the historical origins of instrumental variables regression (Chapter 12).
●Exercise sets provide instructor flexibility in setting up assignments. Review the Conceptsquestions allow students to check their understanding. In addition to Exercises that provide intensive practice, Empirical Exercises allow students to apply what they have learned to answer real-world empirical questions.
Reach every student with MyLab
●The 4th Edition features more exercises covering more topics to allow instructors greater flexibility in assigning auto-graded exercises that provide instant, personalized feedback to students.

本書特色

●Reach every student by pairing this text with MyLab Economics
●Teach methods through real-world questions and applications, and at a mathematical level appropriate for an introductory course.
●Prepare students to work with modern applications and very large data sets, including applications that predict consumer choices and work with nonstandard data (e.g., text data).
●Keep students engaged with a full array of pedagogical material, tools, and resources
●Reach every student with MyLab