- Paperback: 544 pages
- Publisher: Pearson; 1 edition (January 19, 2007)
- Language: English
- ISBN-10: 0321432517
- ISBN-13: 978-0321432513
- Product Dimensions: 7.4 x 1.1 x 9.1 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 13 customer reviews
- Amazon Best Sellers Rank: #1,126,938 in Books (See Top 100 in Books)
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Introduction to Econometrics, Brief Edition 1st Edition
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From the Back Cover
In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that readers apply the theory immediately.Introduction to Econometrics, Brief,is a streamlined version of their text, including the fundamental topics, an early review of statistics and probability, the core material of regression with cross-sectional data, and a capstone chapter on conducting empirical analysis.
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
James Stock chairs the Department of Economics at Harvard University. His research focuses on empirical macroeconomics, forecasting, and econometric methods. Among other things, he has served on the economics panel at the National Science Foundation, on the Academic Advisory Group of the Federal Reserve Bank of Boston, and as a consultant to the European Central Bank. He received his Bachelor’s degree from Yale and holds advanced degrees in statistics and economics from the University of California, Berkeley.
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
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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.
In short, this book might be appropriate as light, supplemental reading, since the exposition and the "intuitions" are occasionally helpful. However, it is simply not sufficient for a college level course. It may be tempting to dumb econometrics down, supplanting rigor and theory with heuristics and "intuition" like this book does, but it really just makes it harder for everyone in the long run. Please, spare students who are interested in econometrics the pain of using such an inadequate book.
There are no examples, and the end of chapter questions don't obviously correlate to a specific place in the chapter that you can refer to in order to facilitate learning the material quickly. Instead, you end up looking through the preceding chapters over and over for sections that kind of tell you what you need to know, but not directly.
When your treasure hunt for simple information fails, you have to refer to stats texts from previous classes, the internet, and everywhere else for help. Even then, there is no way to check yourself while doing the work, so you might have just spent your entire day teaching yourself to do everything the wrong way.
This is a horrible book that only succeeds at forcing you to waste a ton of time looking to outside sources to acquire information that would be readily available if the book was truly written for an introductory econometrics course.