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For courses in introductory econometrics.
This package includes MyLab Economics.
Engaging applications bring the theory and practice of modern econometrics to life
Ensure students grasp the relevance of econometrics with Introduction to Econometrics -- the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics.
Also available with MyLab Economics
By combining trusted author content with digital tools and a flexible platform, MyLab™ personalizes the learning experience and improves results for each student.
Note: You are purchasing a standalone product; MyLab Economics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Economics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.
If you would like to purchase both the physical text and MyLab Economics, search for:
0134610989 / 9780134610986 Introduction to Econometrics Plus MyLab Economics with Pearson eText -- Access Card Package, 4/e
Package consists of:
- 0134461991 / 9780134461991 Introduction to Econometrics
- 0134543939 / 9780134543932 MyLab Economics with Pearson eText -- Access Card -- for Introduction to Econometrics
- Economic Questions and Data
- Review of Probability
- Review of Statistics
- Linear Regression with One Regressor
- Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
- Linear Regression with Multiple Regressors
- Hypothesis Tests and Confidence Intervals in Multiple Regression
- Nonlinear Regression Functions
- Assessing Studies Based on Multiple Regression
- Regression with Panel Data
- Regression with a Binary Dependent Variable
- Instrumental Variables Regression
- Experiments and Quasi-Experiments
- Prediction with Many Regressors and Big Data
- Introduction to Time Series Regression and Forecasting
- Estimation of Dynamic Causal Effects
- Additional Topics in Time Series Regression
- The Theory of Linear Regression with One Regressor
- The Theory of Multiple Regression
- NEW: A new chapter 14 is dedicated to big data and machine learning methods that can help them have much lower out-of-sample prediction errors.
- NEW: Ch. 17 extends the many-predictor focus of Ch. 14 to time series data. Students learn how to forecast future values; an important skill to have as professionals in the field.
- NEW: Regression is now introduced with parallel treatment of prediction and causal inference. This highlights the different demands on how data can be collected (randomized vs. controlled variables).
- UPDATED: General Interest boxes provide students with interesting insight into related topics, while also highlighting real-world studies. Expanded discussions include the historical origins of instrumental variables regression (Ch.12).
- UPDATED: Review the Concepts questions and Exercises let students check their understanding. Empirical Exercises have them apply what they've learned to answer real-world empirical questions.