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创刊号|《机器学习与经济实证应用》教学大纲

来源:南风金融网 作者:南风金融网 人气: 发布时间:2019-09-11 21:02:04

按:本公众号原名“郭峰学术民工”,为上海财经大学公共经济与管理学院投资系副教授郭峰老师的个人公众号,但自即日起更名为“经济数据勘探小分队”,成为学术团队的集体公众号,分享学术团队的原创科研成果、经济数据分析的文献推送和团队的学术活动等信息。欢迎关注。

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作为本公众号的“创刊号”,下面特分享郭峰老师本学期将在上海财经大学开设的课程《机器学习与经济学实证应用》的教学大纲。当前,大数据(Big?Data)已经成为经济金融活动的重要基础和各学科关注的重点。本课程的目的是讲述机器学习的基本原理及其在经济学大数据分析中的应用,使学生能够了解机器学习的基本理念,掌握有监督学习和无监督学习代表性算法的基本原理,并能通过Python语言实现这些算法。最后,通过研读使用机器学习进行实证分析的经济学学术论文,可以将本课程学习到的机器学习原理和算法应用到经济学实证分析当中。

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一、课程名称

机器学习与经济学实证应用

(Machine?Learning?Methods?in Economics)

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二、授课教师

郭 ?峰 ?上海财经大学公共经济与管理学院投资系副教授

办公室:凤凰楼521,E-mail:guo.feng@mail.shufe.edu.cn

个人主页:http://www.guof1984.net/,

公众号:经济数据勘探小分队(guofeng0406)

答疑时间:周二 13:30~17:00?或事先预约

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三、课程类别

选修课

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四、面向对象

博士研究生及硕士研究生

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五、时间地点

时间:周二18:00-20:35(第2-12周),教室:四教405

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六、教学课时数

3 *11=33课时,2学分

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七、预备知识

微观经济学、数学分析、概率统计、中级计量经济学;最好具有一些Python语言基础(第二周周末强化训练)

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八、教学目的

大数据(Big?Data)已经成为经济金融活动的重要基础和各学科关注的重点。本课程的目的是讲述机器学习的基本原理及其在经济学大数据分析中的应用,使学生能够了解机器学习的基本理念,掌握有监督学习和无监督学习代表性算法的基本原理,并能通过Python语言实现这些算法。最后,通过研读使用机器学习进行实证分析的经济学学术论文,可以将本课程学习到的机器学习原理和算法应用到经济学实证分析当中。

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九、考核形式

课堂出勤(20%)、文献汇报+论文推文(30%)、期末论文或研究计划(50%)

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十、内容概要

1、课程导论:从计量经济学到机器学习(3课时)

????(1)参数估计与预测;因果推断与泛化

(2)过拟合、交叉验证

(3)正则化(Regularization):岭回归(Ridge Regression)和LASSO回归

2、有监督学习(Supervised Learning)算法(12课时)

(1)K近邻、贝叶斯分类、线性分类原理(3课时)

(2)分类算法Python实现(3课时)

(3)决策树与随机森林原理与实现(3课时)

(4)支持向量机与神经网络原理与实现(3课时)

4、自然语言处理引论(6课时)

(1)文本向量化与TFIDF关键词提取(2课时)

(2)LDA主题算法原理与Python实现(2课时)

(3)词嵌入算法Python实现(2课时)

5、机器学习经济学论文选读(12课时)

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十一、研读文献

1、机器学习基础

(1)James,?G., Witten, D., Hastie, T., and Tibshirani, R., An Introduction to Statistical Learning,?Springer,?2013. (初级,选读,不认领)

(2)Hastie, T., Tibshirani, R., and Friedman, F.,?The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, 2017. (较难,选读,不认领)

2、综述与概论

(3)Athey, S.,?“The Impact of Machine Learning on Economics”, Working Paper, 2018. (较难,选读,不认领)

(4)Glaeser L. E., Kominers D. S., Luca, M., and Naik, N., “Big Data and Big Cities:The Promises and Limitations of Improved Measures of Urban Life”, Economic Inquiry, 2018,56(1),114-137. (中等,选读,不认领)

(5)Kleinberg, J., Ludwig, J., Mullainathan, S., and Obermeyer, Z., “Prediction Policy Problems”, American Economic Review, 2015, 105(5), 491-95. (中等,选读,不认领)

(6)Loughran, T., and McDonald, B., "Textual Analysis in Accounting and Finance: A Survey." Journal of Accounting Research 54.4 (2016): 1187-1230. (中等,选读,不认领)

(7)Mullainathan, S., and Spiess, J., “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives, 2017, 31(2), 87-106(中等,必读,不认领)

(8)Varian, H.?R., “Big Data: New Tricks for Econometrics”, Journal of Economic Perspectives, 2014, 28(2), 3-28. (中等,选读,不认领)

(9)Gentzkow, M., Kelly,T. B. and Taddy, M., “Text as data”, National Bureau of Economic Research, Working paper, No. w23276, 2017.(较难,选读,不认领)

(10)陈硕、王宣艺,《机器学习在社会科学中的应用:回顾及展望》,复旦大学经济学院工作论文,2018。(初级,必读,不认领)

(11)黄乃静、于明哲,《机器学习对经济学研究的影响研究进展》,《经济学动态》,2018年第7期,第115-129页。(初级,必读,不认领)

(12)沈艳、陈赟、黄卓,《文本大数据分析在经济学和金融学中的应用:一个文献综述》,北京大学国家发展研究院工作论文,2018。(初级,必读,不认领)

3、机器学习与因果推断

(13)Athey, S.,?and?Imbens, G., “Machine Learning Methods for Estimating Heterogeneous Causal Effects”, Statistics, 2015, 113 (27), 7353-7360. (较难,选读,可认领)

(14)Athey, S., Blei, D.,?Donnelly, R., Ruiz, F.,?Schmidt, R., “Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data”, Working Paper, 2018.?(中等,选读,可认领)

(15)Chin, S., Kahn, E. M., and Moon R. H., “Estimating the Gains from New Rail Transit Investment: A Machine Learning Tree Approach”, NBER Working Paper No. 23326, 2017. (中等,选读,可认领)

(16)Linden, A., and Yarnold, R. P., “Combining Machine Learning and Matching Techniques to Improve Causal Inference in Program Evaluation”, Journal of Evaluation in Clinical Practice, 2016, 22, 868-874.(较难,选读,可认领)

(17)Wager,S., and Athey,?S., “Estimation and Inference of Heterogeneous Treatment Effects using Random Forests”, Working Paper, 2017.?(较难,选读,可认领)

4、有监督学习

(18)Basuchoudhary, A., Bang, J., and Sen, T. Machine?Learning?Techniques?in?Economics?New?Tools?for?Predicting?Economic?Growth, Springer, 2017. (多算法,中等,选读,可认领)

(19)Dubé, J., and Misra, S., “Scalable Price Targeting”, NBER Working Paper No. 23775, ?2017. Lasso回归,中等,选读,可认领)

(20)Goel, S., Rao, M. J., and Shroff, R., “Precinct or Prejudice? Understanding Racial Disparities in New York City’s Stop-and-Frisk Policy”, The Annals of Applied Statistics, 2016, 10(1), 365-394. logistic回归,中等,选读,可认领)

(21)Kleinberg, J., Lakkaraju, H. Leskovec, J., Ludwig, J., and Mullainathan, S., “Human Decisions and Machine Predictions”, NBER Working Paper No. 23180, 2017. LASSO-logit回归,中等,选读,可认领)

(22)Bj?rkegren, D., and Grissen, D., “Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment”, Working Paper, 2018.?(随机森林、Logistic回归,初级,选读,可认领)

(23)D?pke, J., Fritsche, U., and Pierdzioch, C., “Predicting Recessions with Boosted Regression Trees”, International Journal of Forecasting,?2017, 33(4), 745-759.?(决策树,中等,选读,可认领)

(24)Burgess,?R., Hansen, M., Olken, A. B., Potapov, P., and Sieber, S., “The Political Economy of Deforestation in the Tropics”, The Quarterly Journal of Economics, 2012, 127(4), 1707-1754. tree-bagging算法,中等,选读,可认领)

(25)Hegazy, O., Soliman, O., and Salam M., “A Machine Learning Model for Stock Market Prediction”, International Journal of Computer Science and Telecommunications, 2013, 4(12), 17-23. (支持向量机,中等,选读,可认领)

(26)Plakandaras, V., Gupta, R., Gogas, P., and Papadimitriou, T. “Forecasting the U.S. Real House Price Index”, Economic Modelling, 2015, 45, 259-267.?(支持向量机,中等,选读,可认领)

5、自然语言处理

(27)Iaria, A., Schwarz, C., and Waldinger, F., “Frontier Knowledge and Scientific Production: Evidence from the Collapse of International Science”, The Quarterly Journal of Economics, 2018, 133(2),927-991.(文本相似度计算,较难,选读,可认领)

(28)Kelly, B., Papanikolaou, D., Seru, A., and Taddy, M., “Measuring Technological Innovation over the Long Run”, NBER Working Paper No. 25266, 2018. (文本相似度计算,中等,选读,可认领).

(29)Antweiler, W., and Frank, M. Z., “Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards”, The Journal of Finance,?2004, 59(3), 1259-1294. (情感分析,朴素贝叶斯算法,中等,选读,不认领)

(30)Bandiera, O., Hansen, S., Prat, A., and Sadun, R., “CEO Behavior and Firm Performance”, NBER Working Paper No. 23248, 2017. (LDA主题模型,中等,选读,可认领)

(31)Mueller, H., and Rauh, C., “Reading Between the Lines: Prediction of Political Violence Using Newspaper Text, American Political Science Review, 2018, 112(2), 358-375.?LDA主题模型,较难,选读,可认领)

(32)Hansen, S., McMahon, M., and Prat, A., “Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach”, The Quarterly Journal of Economics, 2018, 801–870.?LDA主题模型,中等,选读,可认领)

(33)Larseny, H. V., “Components of Uncertainty”, Working Paper, Norges Bank Research, 2017, No.5.?LDA主题模型,推荐星级:****,中等,选读,可认领)

(34)Behrendt, S., and Schmidt, A., “The Twitter Myth Revisited:?Intraday Investor Sentiment, Twitter Activity and Individual-Level Stock Return Volatility”, Journal of Banking and Finance?2018, 96, 355-367. (中等,选读,可认领)

(35)Greenstein, S., Gu, Y. and Zhu, F., “Ideological Segregation Among Online Collaborators: Evidence from Wikipedians”, NBER Working Paper No. 22744, 2016.

(36)Sun, L., Najand, M., and Shen, J. “Stock Return Predictability and Investor Sentiment: A High-frequency Perspective, Journal of Banking and Finance, 2016, 73, 147–164 (中等,选读,可认领)

(37)Tsukioka, Y., Yanagi, J., and Takada, T., “Investor Sentiment Extracted from Internet Stock Message Boards and IPO Puzzles”, International Review of Economics and Finance, 2018, 56, 205-217. (支持向量机,中等,选读,可认领)

(38)王靖一、黄益平,《金融科技媒体情绪的刻画与对网贷市场的影响》,《经济学季刊》,2018年第17卷第4期,第1623-1650页。(多算法,较难,选读,可认领)

(39)Zhong, W., and Chan J. T., “Reading China: Predicting Policy Change with Machine Learning”, AEI Economics Working Paper Series,?2018-11.(神经网络;中等,选读,可认领)

(40)Kima, S. H., and Kim, D., “Investor Sentiment from Internet Message Postings and the Predictability of Stock Returns, Journal of Economic Behavior & Organization, 2014, 107, 708–729. (朴素贝叶斯,中等,选读,不认领)

(41)Scott, R. B., Nicholas, B., and Steven J. D., "Measuring Economic Policy Uncertainty." Quarterly Journal of Economics, 2016, 131(4), 1593-1636.

(42)Manela, A., and Alan Moreira, A., “News Implied Volatility and Disaster Concerns”, Journal of Financial Economics, 2017, 123, 137-162.(隐含波动率,中等,选读,可认领)

(43)陈霄、叶德珠、邓洁,《借款描述的可读性能够提高网络借款成功率吗》,《中国工业经济》,2018年第3期,第174-192页。(文本可读性,中等,选读,可认领)

(44)丘心颖、郑小翠、邓可斌,《分析师能有效发挥专业解读信息的作用吗?——基于汉字年报复杂性指标的研究》,《经济学季刊》,2016年第15卷第4期,第1483-1506页。(文本可读性,中等,选读,可认领)


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