課程資訊
課程名稱
 應用選擇資料分析
Applied Discrete Choice Analysis 
開課學期
 112-2 
授課對象
 生物資源暨農學院  農業經濟學系  
授課教師
 石曜合 
課號
 AGEC5045 
課程識別碼
 627EU1200 
班次
  
學分
 3.0 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期一7,8,9(14:20~17:20) 
上課地點
 農經四 
備註
 本課程以英語授課。建議具備計量經濟相關知識，
限學士班學生
總人數上限：4人 
  
課程簡介影片
 
核心能力關聯
 本課程尚未建立核心能力關連
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課程概述

Discrete choice models (DCM) have been widely applied to study individual choice behavior problems in many fields such as economics, marketing, environmental management, and transportation. This course will mainly focus on the methods and applications of DCM on topics in agricultural and environmental economics. For example, how people choose their vacation destinations, select the ideal environmental programs/policies, and pick the food product they want. 

課程目標
 The course first introduces the theories and framework of DCM, data collection for DCM, as well as how DCM has been applied in various disciplines. The course covers some of the fundamental discrete choice models (logit, nested logit, probit, and mixed logit) and includes lab sessions where students can learn how to analyze how people making choices using real datasets. The principle software used is R. The primary goal of the course is for students to gain hands-on experience in using discrete choice techniques for practical applications.

This course is not simply about how to analyze discrete choice data nor how to use R, but about the entire process of conducting empirical projects using discrete choice models. The objectives of the course are helping students to develop the ability to explore questions, find data, analyze the data with appropriate methods, interpret the results, and justify the contributions. 
課程要求
 * In-class participation: 30%
* Paper review and seminar: 30%
* Project proposal or Replication project: 40%

Students are expected to attend regular class time lectures and lab sessions. In addition, students are expected to actively participate in class and complete a paper review and a project proposal (or replication project). Problem sets will be assigned throughout the semester for practice.

Students are expected to write a brief project proposal for an empirical choice analysis project. The proposal has to address the following: (1) what the research question is, (2) what have been done in the literature, (3) how the data can be collected (or where the data are), (4) the analytic framework and models, and (5) the expected findings and why the question and findings are interesting and/or useful. Students are strongly encouraged to meet with the instructor before the mid-term to discuss the proposal's topic. The proposal is due at the end of 6/3 (the final week).

Project proposal can be substituted by replicating the results of a paper listed on the reading list. The grade will be largely based on how well the results are replicated. In the most ideal cases, students are expected to reproduce ALL tables in the main text. The numbers should match the original results to a certain level of precision. That being said, students are not likely to get a good grade by producing a table of summary statistics and another table with only a mixed logit model. In case some of the results cannot be replicated, especially because the models are not included in any R packages (so one needs to write the MLE functions or such), students would want to use the best available models and discuss the causes of the discrepancies. If students want to replicate the results of a paper that is not on the list, please email the instructor. The replication project is also due at the end of 6/3 (the final week).

The work outside of class is typically three to six hours per week. Grading for the class will follow the University's ranking and percentile score system.

The instructor encourages feedback throughout the semester to make sure the course goals and students' expectations are being met. 
預期每週課後學習時數
 3 - 6 hours 
Office Hours
 另約時間 
指定閱讀
 Train, K. E. (2009). Discrete choice methods with simulation, 2nd. Edition. Cambridge University Press.
Available at https://eml.berkeley.edu/books/choice2.html 
參考書目
 The following books are recommended for reference.

* For discrete choice modeling

Champ. P. A., Boyle., K. J., & Brown T. C. (Eds.) (2017). A Primer on Nonmarket Valuation, 2nd Edition. Dordrecht: Springer Netherlands. (Available via NTU library eBooks)

Mariel, P., Hoyos, D., Meyerhoff, J., Czajkowski, M., Dekker, T., Glenk, K., ... & Thiene, M. (2021). Environmental valuation with discrete choice experiments: Guidance on design, implementation and data analysis. Springer Nature. (Open access)

Kleiber, C., & Zeileis, A. (2008). Applied Econometrics with R. Springer Science & Business Media. (Available via NTU library eBooks)

* For R programming and other relevant topics (e.g., machine learning)

R Cookbook: https://rc2e.com/

Hands-On Programming with R: https://rstudio-education.github.io/hopr/

R for Data Science: https://r4ds.had.co.nz/

Hands-On Machine Learning with R: https://bradleyboehmke.github.io/HOML/

An Introduction to Statistical Learning: http://faculty.marshall.usc.edu/gareth-james/ISL/

* Course handouts, slides, and data will be made available at the course website by the instructor. 
評量方式
(僅供參考)
    
課程進度
週次
日期
單元主題
Week 1
 2/19  Course overview: the science of choosing 
Week 2
 2/26  Mixtape Session: Demand Estimation (external online lectures) 
Week 3
 3/4  Properties of discrete choice models; Random utility model 
Week 4
 3/11  The fundamental in discrete choice: Logit 
Week 5
 3/18  When IIA fails: Nested Logit and GEV models 
Week 6
 3/25  Accommodating random preference heterogeneity and repeated choices: Probit
The current standard in DCM: mixed logit 
Week 7
 4/1  Mixtape Session: Demand Estimation (external online lectures) 
Week 8
 4/8  Mixtape Session: Demand Estimation (external online lectures) 
Week 9
 4/15  DCM in ag and env econ (I): Revealed preference
 
Week 10
 4/22  DCM in ag and env econ (II): Stated preference 
Week 11
 4/29  Endogeneity in DCM; ordered choices and outcomes; some research frontiers 
Week 12
 5/6  Student Seminar (I & II & III) 
Week 13
 5/13  Student Seminar (IV & V & VI) 
Week 14
 5/20  Student Seminar (VII & VIII) 
Week 15
 5/27  Research Proposal Presentation 
Week 16
 6/3  Final (No Class; Proposal Due)