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課程資訊

課程名稱	應用選擇資料分析 Applied Discrete Choice Analysis
開課學期	110-2
授課對象	生物資源暨農學院農業經濟學研究所
授課教師	石曜合
課號	AGEC7144
課程識別碼	627EM5290
班次
學分	3.0
全/半年	半年
必/選修	選修
上課時間	星期四3,4,5(10:20~13:10)
上課地點	共207
備註	本課程以英語授課。建議具備計量經濟相關知識，限碩士班以上總人數上限：12人
課程網頁	https://cool.ntu.edu.tw/courses/4471/assignments/syllabus
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. 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 6/2 (the final week). The instructor will email the feedback for the proposal within 2 weeks after the due date. Project proposal may 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 6/2 (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
Office Hours
指定閱讀	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.
參考書目	Train, K. E. (2009). Discrete Choice Methods with Simulation, 2nd Edition. Cambridge University Press. (Available online at https://eml.berkeley.edu/books/choice2.html )
評量方式 (僅供參考)

課程進度

週次	日期	單元主題
Week 1	02/17	Course overview: the science of choosing
Week 2	02/24	Properties of discrete choice models; Random utility model
Week 3	03/03	The fundamental in discrete choice: Logit
Week 4	03/10	When IIA fails: Nested Logit and GEV models
Week 5	03/17	Accommodating random preference heterogeneity and repeated choices: Probit
Week 6	03/24	The current standard in DCM: mixed logit (I)
Week 7	03/31	Mixed logit (II) and count models
Week 8	04/07	Mid-term (No class)
Week 9	04/14	DCM in ag and env econ (I): Revealed preference * Seminar paper selection due
Week 10	04/21	DCM in ag and env econ (II): Stated Preference * Final proposal topic due
Week 11	04/28	Student seminar(s) Nudging Energy Efficiency Behavior: The Role of Information Labels (陳禹嫺) Risk Preferences, Risk Perceptions, and Flood Insurance (傅羿寧)
Week 12	05/05	Ordered choices and outcomes Some alternatives to RUM Student seminar(s) Sad or Happy? The effects of emotions on stated preferences for environmental goods (王冠傑) An experiment on the vote-buy gap with application to cage-free eggs (吳子欣)
Week 13	05/12	Endogeneity in DCM and other issues in DCM Student seminar(s) Using revealed preferences to estimate the value of travel time to recreation sites (蕭雲豪)
Week 14	05/19	Statistical learning and discrete choice analysis Proposal Presentations
Week 15	05/26	Proposal Presentations
Week 16		Final Week (no class)