Julian TszKin Chan

Bates White Economic Consulting

1300 Eye Street NW

Suite 600

Washington, DC 20005

United States

SCHOLARLY PAPERS

5

DOWNLOADS

421

SSRN CITATIONS

1

CROSSREF CITATIONS

1

Scholarly Papers (5)

1.

Reading China: Predicting Policy Change with Machine Learning

AEI Economics Working Paper Series (No. 2018-11)
Number of pages: 43 Posted: 06 Dec 2018 Last Revised: 14 Apr 2019
Julian TszKin Chan and Weifeng Zhong
Bates White Economic Consulting and Mercatus Center at George Mason University
Downloads 227 (171,853)
Citation 4

Abstract:

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policy change, machine learning, China, People’s Daily, propaganda

2.

Snowball Sampling and Sample Selection in a Social Network

Number of pages: 23 Posted: 08 May 2019
Julian TszKin Chan
Bates White Economic Consulting
Downloads 127 (282,154)

Abstract:

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3.

Predicting Authoritarian Crackdowns: A Machine Learning Approach

Mercatus Research Paper
Number of pages: 31 Posted: 02 Mar 2020
Julian TszKin Chan and Weifeng Zhong
Bates White Economic Consulting and Mercatus Center at George Mason University
Downloads 38 (539,755)

Abstract:

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policy change, machine learning, protest, crackdown, propaganda

4.

Words Speak Louder Than Numbers: Estimating China’s COVID-19 Severity with Deep Learning

Mercatus COVID-19 Response Working Paper Series
Number of pages: 31 Posted: 29 Dec 2020
Julian TszKin Chan, Kwan-Yuet Ho, Kit Lee, Weifeng Zhong and Kawai Leung
Bates White Economic Consulting, Data Scientist, Data Scientist, Mercatus Center at George Mason University and Independent Researcher
Downloads 29 (588,850)

Abstract:

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policy change, propaganda, deep learning, coronavirus, COVID-19, SARS, outbreak

5.

Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach

Journal of Risk and Financial Management, 14(9), 423. https://doi.org/10.3390/jrfm14090423, The University of Auckland Business School Research Paper Series
Posted: 30 Nov 2021
William Cheung, Julian TszKin Chan, Sijie Li and Edward Chung Yim Yiu
The University of Auckland Business School, Bates White Economic Consulting, University of Pittsburgh and University of Auckland Business School

Abstract:

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unsupervised machine learning, natural language process, non-local buyers, anchoring biases, information asymmetry, repeat-sales estimates