Introduction to Econometrics, 4/e (Paperback)
暫譯: 計量經濟學導論（第4版，平裝本）

James H. Stock , Mark W. Watson

Introduction to Econometrics, 4/e (Paperback)

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本書序言

Prepare students to work with modern applications and very large data sets, including applications that predict consumer choices and work with nonstandard data (e.g., text data).
●A new Chapter 14 is dedicated to big data and machine learning methods. In economics, many applications focus on the “many-predictor” problem, where the number of predictors is large relative to the sample size. This chapter introduces students to methods beyond the ordinary least squares method that can help them have much lower out-of-sample prediction errors.
●Chapter 17 extends the many-predictor focus of Chapter 14 to time series data. Using the dynamic factor model and a 131-variable set of US quarterly macroeconomic data, students learn how to forecast future values — an important skill to have as professionals in the field of econometrics.
●Regression is now introduced with a parallel treatment of prediction and causal inference, to expose students to the different demands on how data can be collected (i.e., randomized vs. controlled variables).
Keep students engaged with a full array of pedagogical material, tools, and resources
●General Interest boxes provide students with interesting insight into related topics, while also highlighting real-world studies. The 4th Edition now extends discussion of the historical origins of instrumental variables regression (Chapter 12).
●Exercise sets provide instructor flexibility in setting up assignments. Review the Conceptsquestions allow students to check their understanding. In addition to Exercises that provide intensive practice, Empirical Exercises allow students to apply what they have learned to answer real-world empirical questions.
Reach every student with MyLab
●The 4th Edition features more exercises covering more topics to allow instructors greater flexibility in assigning auto-graded exercises that provide instant, personalized feedback to students.

本書特色

●Reach every student by pairing this text with MyLab Economics
●Teach methods through real-world questions and applications, and at a mathematical level appropriate for an introductory course.
●Prepare students to work with modern applications and very large data sets, including applications that predict consumer choices and work with nonstandard data (e.g., text data).
●Keep students engaged with a full array of pedagogical material, tools, and resources
●Reach every student with MyLab

商品描述(中文翻譯)

**本書序言**

**準備學生應對現代應用程式和非常大的數據集，包括預測消費者選擇和處理非標準數據（例如，文本數據）的應用程式。**
● 新增的第14章專注於大數據和機器學習方法。在經濟學中，許多應用聚焦於「多預測變數」問題，即預測變數的數量相對於樣本大小而言非常大。本章介紹了超越普通最小二乘法的方法，幫助學生降低樣本外預測誤差。
● 第17章將第14章的多預測變數焦點擴展到時間序列數據。使用動態因子模型和131個變數的美國季度宏觀經濟數據集，學生學習如何預測未來值——這是作為計量經濟學專業人士必備的重要技能。
● 現在以平行的方式介紹迴歸，將預測和因果推斷並行處理，讓學生了解數據收集的不同要求（即隨機變數與控制變數）。
**透過全面的教學材料、工具和資源保持學生的參與感**
● 一般興趣框提供學生對相關主題的有趣見解，同時突顯現實世界的研究。第4版現在擴展了對工具變數迴歸歷史起源的討論（第12章）。
● 練習集為教師提供靈活性以設置作業。概念檢查問題讓學生檢查自己的理解。除了提供密集練習的練習題外，實證練習讓學生應用所學知識來回答現實世界的實證問題。
**透過MyLab接觸每位學生**
● 第4版包含更多涵蓋更多主題的練習，讓教師在分配自動評分練習時有更大的靈活性，並為學生提供即時的個性化反饋。

**本書特色**

● 透過將本書與MyLab Economics結合，接觸每位學生
● 通過現實世界的問題和應用教授方法，並在適合入門課程的數學水平上進行
● 準備學生應對現代應用程式和非常大的數據集，包括預測消費者選擇和處理非標準數據（例如，文本數據）的應用程式
● 透過全面的教學材料、工具和資源保持學生的參與感
● 透過MyLab接觸每位學生

目錄大綱

Part One. Introduction and Review
1. Economic Questions and Data
2. Review of Probability
3. Review of Statistics
Part Two. Fundamentals of Regression Analysis
4. Linear Regression with One Regressor
5. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
6. Linear Regression with Multiple Regressors
7. Hypothesis Tests and Confidence Intervals in Multiple Regression
8. Nonlinear Regression Functions
9. Assessing Studies Based on Multiple Regression
Part Three. Further Topics in Regression Analysis
10. Regression with Panel Data
11. Regression with a Binary Dependent Variable
12. Instrumental Variables Regression
13. Experiments and Quasi-Experiments
14. Prediction with Many Regressors and Big Data
Part Four. Regression Analysis of Economic Time Series Data
15. Introduction to Time Series Regression and Forecasting
16. Estimation of Dynamic Causal Effects
17. Additional Topics in Time Series Regression
Part Five. Regression Analysis of Economic Time Series Data 
17. The Theory of Linear Regression with One Regressor
18. The Theory of Multiple Regression 

目錄大綱(中文翻譯)

Part One. Introduction and Review

1. Economic Questions and Data

2. Review of Probability

3. Review of Statistics

Part Two. Fundamentals of Regression Analysis

4. Linear Regression with One Regressor

5. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals

6. Linear Regression with Multiple Regressors

7. Hypothesis Tests and Confidence Intervals in Multiple Regression

8. Nonlinear Regression Functions

9. Assessing Studies Based on Multiple Regression

Part Three. Further Topics in Regression Analysis

10. Regression with Panel Data

11. Regression with a Binary Dependent Variable

12. Instrumental Variables Regression

13. Experiments and Quasi-Experiments

14. Prediction with Many Regressors and Big Data

Part Four. Regression Analysis of Economic Time Series Data

15. Introduction to Time Series Regression and Forecasting

16. Estimation of Dynamic Causal Effects

17. Additional Topics in Time Series Regression

Part Five. Regression Analysis of Economic Time Series Data 

17. The Theory of Linear Regression with One Regressor

18. The Theory of Multiple Regression 

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