Introductory econometrics : a modern approach
著者
書誌事項
Introductory econometrics : a modern approach
Cengage, c2020 [sic]
7th ed
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注記
Includes bibliographical references (p. 791-796) and index
内容説明・目次
内容説明
Gain an understanding of how econometrics can answer today's questions in business, policy evaluation and forecasting with Wooldridge's INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 7E. Unlike traditional texts, this book's practical, yet professional, approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines.
The author has organized the book's presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field, including the recent advances in the so-called �causal effects� or �treatment effects," to provide a complete understanding of the impact and importance of econometrics today.
目次
1. The Nature of Econometrics and Economic Data.
Part I: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA.
2. The Simple Regression Model.
3. Multiple Regression Analysis: Estimation.
4. Multiple Regression Analysis: Inference.
5. Multiple Regression Analysis: OLS Asymptotics.
6. Multiple Regression Analysis: Further Issues.
7. Multiple Regression Analysis with Qualitative Information.
8. Heteroskedasticity.
9. More on Specification and Data Problems.
Part II: REGRESSION ANALYSIS WITH TIME SERIES DATA.
10. Basic Regression Analysis with Time Series Data.
11. Further Issues in Using OLS with Time Series Data.
12. Serial Correlation and Heteroskedasticity in Time Series Regressions.
Part III: ADVANCED TOPICS.
13. Pooling Cross Sections Across Time: Simple Panel Data Methods.
14. Advanced Panel Data Methods.
15. Instrumental Variables Estimation and Two Stage Least Squares.
16. Simultaneous Equations Models.
17. Limited Dependent Variable Models and Sample Selection Corrections.
18. Advanced Time Series Topics.
19. Carrying Out an Empirical Project.
Math Refresher A: Basic Mathematical Tools.
Math Refresher B: Fundamentals of Probability.
Math Refresher C: Fundamentals of Mathematical Statistics.
Math Refresher D: Summary of Matrix Algebra.
Math Refresher E: The Linear Regression Model in Matrix Form.
Answers to Exploring Further Chapter Exercises.
Statistical Tables.
References.
Glossary.
Index.
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