题目：Learning and Subjective Expectation Formation: A Recurrent Neural Network Approach

主讲人：Chenyu(Sev) Hou (Ph.D. Candidate at UBC)

主持老师：余淼杰、余昌华（北京大学国家发展研究院）

时间：2021年6月1日（周二）上午10:30-12:00

线下：朗润园致福轩会议室

线上：加入 Zoom 会议https://zoom.com.cn/j/95365409407?pwd=cVVFYzZRRFBQVmwycTE2UTBHWTh2dz09

会议号：953 6540 9407

密码：513486

摘要：I propose a flexible non-parametric method using Recurrent Neural Networks (RNN) to estimate a generalized model of expectation formation. This approach does not rely on restrictive assumptions of functional forms and parametric methods yet nests the standard approaches of empirical studies on expectation formation. Applying this approach to data on macroeconomic expectations from the Michigan Survey of Consumers (MSC) and a rich set of signals available to U.S. households, I document three novel findings: (1) agents’ expectations about the future economic condition have asymmetric and non-linear responses to signals; (2) agents’ attentions shift from signals about the current state to signals about the future: they behave as adaptive learners in ordinary periods and become forward-looking as the state of economy gets worse; (3) the content of signals on economic conditions, rather than the amount of news coverage on these signals, plays the most important role in creating the attention-shift. Double Machine Learning approach is then used to obtain statistical inferences of these empirical findings. Finally, I show these stylized facts can be generated by a model with rational inattention, in which information endogenously becomes more valuable when economic status worsens.

主讲人简介：Chenyu(Sev) Hou is a Ph.D. Candidate in Economics at University of British Columbia, and he is gonging to be an Assistant Professor at Chinese University of Hong Kong, Shenzhen. He obtained M.S. degree at University of Texas at Austin and M.A. degree at Simon Fraser University. He graduated from Fudan University with B.A.. His primary fields are Macroeconomics, Monetary Economics and Applied Econometrics. His secondary fields are Expectation Formation, Information Acquisition and Deep Learning.