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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.
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- Sales Rank: #71683 in Books
- Published on: 2007-01-19
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .90" w x 7.30" l, 1.78 pounds
- Binding: Paperback
- 544 pages
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.
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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.
Most helpful customer reviews
8 of 8 people found the following review helpful.
Depends on who you're teaching.
By not a natural
This text is unusually brief when compared to most econometrics texts, and the brevity reflects the coverage of topics. Nevertheless, the authors have done a favor for those of us who have to teach regression to students without strong backgrounds in statistics, and whose interests do not take them into comparatively esoteric material.
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.
14 of 16 people found the following review helpful.
Would not recommend
By shma
The authors have adopted a very awkward approach: They exclude much statistical theory and any math beyond basic algebra, but they still try to incorporate a little bit of rigor and some formal (or quasi-formal) definitions here and there. The result is just confusing. Their unsuccessful attempt at balancing between intuition and rigor is not helped by the writing itself, which is clumsy, or the generally uninteresting examples provided. (Note: Some additional explanations and derivations are included in various appendices, but they are hard to find and generally not very detailed.) IMHO, Wooldridge's "Introductory Econometrics" does a much better job conveying the underlying concepts at a comparable level, has better examples, and is much more pleasant to read.
7 of 7 people found the following review helpful.
Great for an introductory class with less complex math skills
By Billy Zeng
I am using this book as a self study guide to run regression on personal projects. I find that what it lacks in math rigor as compared to Wooldridge's Introductory Econometrics, it far than make up for in data interpretation. It is a mistake to use this book for students who have advanced math skills such as probability theory and multivariate calculus. But for business or just applications it is superior.
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