本課程教學大綱已提供完整英文資訊（本選項僅供統計使用，未提供完整英文資訊者，得免勾記）【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】
This course will teach you quantitative research methods when you conduct an industrial analysis. I will also take several types of industrial datasets as examples to discuss how to apply these quantitative methods. Because our discussion in this course is based on the concepts in economics and statistics, I strongly suggest you have a good understanding in economics (microeconomics or managerial economics) and statistics before you take this course. This course has two parts: basic analysis tool/quantitative method, and different types of datasets you will possibly face and advanced quantitative method to deal with these data. In the basic part, I will focus on regression analysis. We will discuss how to use this method, and how to properly interpret the regression result; I will then introduce topics regarding endogeneity, nonlinear regression, and interaction terms. In the data part, I will introduce several types of datasets, including time-series cross-sectional data, housing or artwork transaction data, market equilibrium data, and business registration data. While introducing these datasets, we will discuss the proper way to observe/interpret these data, and advanced methods to deal with these data. These methods some of which can help you to do a proper casual inference include difference-in-differences estimation (DID), propensity score matching (PSM), instrumental variable regression, quantile regression, and survival analysis. Moreover, to let you have an opportunity to apply these quantitative methods to practice, you need to complete a group project. In this project, you have to pick an industry first. Then, you have to collect data, and try to find the demand or supply curve, or try to answer relevant questions in that industry via data. You can learn how to make decisions based on evidence; understand the idea of evidence-based management.
課程目標 Objectives
1. Understand the quantitative methods in industrial analysis. 2. Know how to use market or industrial data to get market or industrial insights. 3. Know how to answer managerial questions via data.
參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念，不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use
1. Required reading 1A. James H. Stock and Mark W. Watson (2010) 胥愛琦與呂瓊瑜譯。計量經濟學。台北：台灣培生教育 (ISBN: 978-986-280-021-8)。 1B. Related papers or industry analysis reports, which will be regularly posted on the National Sun Yat-sen Cyber University 2. Reference 2A. 陳正倉、林惠玲、陳忠榮與莊春發 (2014)。產業經濟學。台北：雙葉書廊 (ISBN: 978-986-7433-80-0)。
每週課程內容及預計進度 Weekly scheduled progress
週次
日期
授課內容及主題
Week
Date
Content and topic
1
2023/02/12~2023/02/18
Course Introduction
2
2023/02/19~2023/02/25
Basic Tool: Regression Analysis
3
2023/02/26~2023/03/04
Basic Tool: Regression Analysis
4
2023/03/05~2023/03/11
Basic Tool: Regression Analysis and Causal Inference
5
2023/03/12~2023/03/18
Basic Tool: Regression Analysis and Causal Inference
6
2023/03/19~2023/03/25
Basic Tool: Regression Analysis and Nonlinear Models
7
2023/03/26~2023/04/01
Midterm
8
2023/04/02~2023/04/08
Time-series Cross-sectional Data: Fixed Effects Model, DID Estimator, and PSM
9
2023/04/09~2023/04/15
Housing or Artwork Transaction Data: Hedonic Pricing Method
Internship: The required or elective courses should include credits and learning hours. Students should participate in the corporative company or institution to practice and learn the real skills. An internship certification must be handed in at the end of internship to get the credits or to fulfil the graduation requirements.