課程名稱 |
計量經濟學計算方法 Computational Methods for Econometrics |
開課學期 |
108-2 |
授課對象 |
社會科學院 經濟學研究所 |
授課教師 |
謝志昇 |
課號 |
ECON7218 |
課程識別碼 |
323EM3770 |
班次 |
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學分 |
3.0 |
全/半年 |
半年 |
必/選修 |
選修 |
上課時間 |
星期一2,3,4(9:10~12:10) |
上課地點 |
社科401 |
備註 |
本課程以英語授課。 限碩士班以上 總人數上限:20人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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課程概述 |
In modern economic research, computers enhance our capacity of analyzing complex problems with data support. Computation is particularly important in fields involving dynamic modeling, structural equations, and massive data, such as macro, labor, and industrial organization. However, computational methods have not been part of the core curriculum of postgraduate-level economics education, whereas programming skills are critical for a postgraduate’s success in academia and industry. The objective of this course is to introduce graduate students to commonly applied computational approaches for solving econometric models, with an emphasis on numerical optimization, Bayesian MCMC, simulation-based methods, and dynamic programming.
We expect that at the end of the course a student would proficiently use at least one programming language (Stata, Matlab, R, etc). Moreover, we aim to equip the students with the computational ability to tackle problems of their own research areas.
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課程目標 |
This course intends to introduce students with computational methods for solving econometric problems, and expose students to extensive programming exercises. After completing this course, students should
1. have intermediate skills on using STATA, R, and MATLAB.
2. be familiar with well-known computational methods used in the current literature
3. be able to explore and potentially solve computational challenges faced by their own research.
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課程要求 |
待補 |
預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
待補 |
參考書目 |
1. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and Applications. Cambridge university press.
2. Cameron, A. C., & Trivedi, P. K. (2009). Microeconometrics Using Stata (Vol. 5, p. 706). College Station, TX: Stata press.
3. Koop, G., Poirier, D. J., & Tobias, J. L. (2007). Bayesian Econometric Methods. Cambridge University Press.
4. Judd, Kenneth (1998): Numerical Methods in Economics, the MIT Press
5. Miranda, M. and Fackler, P. (2002) Applied Computational Economics and Finance. MIT
6. Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.
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評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
course assignments |
50% |
There will be several assignments to exercise the problem solving and software coding |
2. |
presentation |
50% |
Students should give a presentation of one paper chosen from the reading list. |
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週次 |
日期 |
單元主題 |
第1週 |
3/02 |
Numerical optimization (linear equation, linear programming) |
第2週 |
3/09 |
Numerical optimization (general nonlinear optimization) |
第3週 |
3/16 |
Bayesian estimation (theory) |
第4週 |
3/23 |
Bayesian estimation (Markov chain Monte Carlo sampling) |
第5週 |
3/30 |
Bayesian estimation (Markov chain Monte Carlo sampling) |
第6週 |
4/06 |
Introduction to STATA (basic commends and examples) |
第7週 |
4/13 |
Student presentation |
第8週 |
4/20 |
Student presentation |
第9週 |
4/27 |
Bootstrap |
第10週 |
5/04 |
Introduction to R (basic commends and examples) |
第11週 |
5/11 |
Simulated-based estimation (Numerical Integration) |
第12週 |
5/18 |
Simulated-based estimation (EM, SML and SMM, etc.) |
第13週 |
5/25 |
Dynamic programming |
第14週 |
6/01 |
Dynamic programming |
第15週 |
6/08 |
Student Presentation |
第16週 |
6/15 |
Student Presentation |
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