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Introduction to Econometrics, Brief Edition 1st Edition
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From the Back Cover
Introduction and Review:Economic Questions and Data; Review of Probability; Review of Statistics.Fundamentals of Regression Analysis:Linear Regression with One Regressor; Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals in the Single-Regressor Model; Linear Regression with Multiple Regressors; Hypothesis Tests and Confidence Intervals in the Multiple Regressor Model; Nonlinear Regression Functions; Assessing Studies Based on Multiple Regression; Conducting a Regression Study Using Economic Data.
MARKET: For all readers interested in econometrics.
About the Author
Mark Watson is the Howard Harrison and Gabrielle Snyder Beck Professor of Economics and Public Affairs at Princeton University and a research associate at the National Bureau of Economic Research. He is a fellow of the American Academy of Arts and Sciences and of the Econometric Society. His research focuses on time-series econometrics, empirical macroeconomics, and macroeconomic forecasting. He has served as a consultant for the Federal Reserve Banks of Chicago and Richmond. Before coming to Princeton, Watson served on the economics faculty at Harvard and Northwestern. Watson did his undergraduate work at Pierce Junior College and California State University at Northridge, completed his Ph.D. at the University of California at San Diego, and holds on honorary doctorate from the University of Bern.
Top Customer Reviews
As it is, the book does a good job of covering basic issues essential to using OLS regression for applied work in most institutional settings. The book is written so that students find it more accessible than other texts, though most still find it pretty heavy going. The book is replete with graphs and charts, clarifying important issues. Examples are numerous and instructive, as are questions for students to answer and problems for them to solve.
More and more academic majors are requiring their students to gain a working knowledge of regression analysis. Stock and Watson's book makes that task less onerous, and it recognizes that most students will not have to go beyond the core topics needed to work with useful but relatively simple models. A good book.
Leaves much to be desired... I was lost in the lectures, so this book got me through some of the concepts, but it could have been better. (In comparison, for a previous stat course I used the book 'Statistics' by Freedman, which used excellent visuals and graphs to really drive the points home.)
If you have another option, take that option. If not, supplement (or supplant) with another book or online resources.
My biggest gripe is that there are few examples. I really took this for granted, and didn't notice how important it was until they were taken out. In classes like math, you can easily get lost in all the notation in each formula. Examples are important because they show you how to use that formula in a real application. The book lacks examples, and this really makes it harder to understand what Stock and Watson are talkin about.
Another thing I'd like are answers to their problems. This is just useful so that you can check whether you are doing the problems at the end of the chapter right. Otherwise, you're completely clueless on how well you are doing.
Most Recent Customer Reviews
Used this book in my Econometrics course. The chapters flowed together well in a natural progression starting with a review of basic statistics and probability, then linear... Read morePublished 20 days ago by Ankur Kansal
Good introductory text. Provides good overview and helps drive home main ideas behind the math.Published 5 months ago by Amazon Customer
The book was came with the binding nearly torn all the way off, but I guess that's the risk of buying used. Otherwise, it's a pretty standard textbook.Published 6 months ago by Amazon Customer
Great book. Even though a difficult concept, they simplify it while still retaining import substance.Published 10 months ago by Alejandra Gimenez