Introduction to Econometrics, 4/e (Paperback)

James H. Stock , Mark W. Watson

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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資料集,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。
● 新增第14章專門介紹大數據和機器學習方法。在經濟學中,許多應用都集中在“多預測變數”問題上,即預測變數的數量相對於樣本量很大。本章介紹了超出普通最小二乘法的方法,可以幫助學生在預測時具有更低的樣本外預測誤差。
● 第17章將第14章的多預測變數焦點擴展到時間序列數據。通過使用動態因子模型和一組131個美國季度宏觀經濟數據,學生學習如何預測未來值-這是作為計量經濟學領域專業人員的重要技能。
● 現在以平行處理的方式介紹回歸,包括預測和因果推斷,以使學生了解數據收集的不同要求(即隨機化對照變量)。

通過全面的教學材料、工具和資源,保持學生的參與度。
● 一般興趣框提供有趣的相關主題見解,同時突出現實世界的研究。第四版現在擴展了工具變數回歸的歷史起源討論(第12章)。
● 練習題組提供教師在設定作業方面的靈活性。概念回顧問題讓學生檢查他們的理解。除了提供密集練習的練習題外,實證練習題還允許學生應用所學知識回答現實世界的實證問題。

通過MyLab,觸及每一位學生。
● 第四版增加了更多涵蓋更多主題的練習題,讓教師在指派自動評分的練習題方面具有更大的靈活性,並為學生提供即時個性化的反饋。

本書特色

通過與MyLab經濟學配對,觸及每一位學生。
以適合入門課程的數學水平,通過現實世界的問題和應用來教授方法。
準備學生應對現代應用程式和非常大的資料集,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。
通過全面的教學材料、工具和資源,保持學生的參與度。
通過MyLab,觸及每一位學生。

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)

第一部分:介紹和回顧
1. 經濟問題和數據
2. 概率回顧
3. 統計回顧

第二部分:回歸分析基礎
4. 帶有一個自變量的線性回歸
5. 帶有單個自變量的回歸:假設檢驗和置信區間
6. 帶有多個自變量的線性回歸
7. 多元回歸中的假設檢驗和置信區間
8. 非線性回歸函數
9. 基於多元回歸的研究評估

第三部分:回歸分析的進一步主題
10. 面板數據回歸
11. 二元因變量回歸
12. 儀器變量回歸
13. 實驗和準實驗
14. 帶有多個自變量和大數據的預測

第四部分:經濟時間序列數據的回歸分析
15. 時間序列回歸和預測介紹
16. 動態因果效應的估計
17. 時間序列回歸的其他主題

第五部分:經濟時間序列數據的回歸分析
17. 帶有一個自變量的線性回歸理論
18. 多元回歸的理論