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Learning from Online Ratings
Learning from Online Ratings
Learning from Online Ratings
Learning from Online Ratings
31May
Seminar Calendar, Marketing

Learning from Online Ratings

31 May, 2024 | 10:30 a.m. - 12:00 p.m.
KK 910, K. K. Leung Building, HKU
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SPEAKER

Prof. Xiang Hui
Assistant Professor of Marketing
OLIN Business School
Washington University in St.Louis

ABSTRACT

Online ratings play an important role in many markets. However, how fast they can reveal seller types remains unclear. We propose a simple model of rating behavior where learning about the seller type influences the rating decision. We calibrate the model to eBay data and find that ratings can be very informative. After 25 transactions, the likelihood of correctly predicting the seller type is above 95 percent.

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Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
5Jun
Innovation and Information Management | Seminar
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
5 June 2025 | 11:00 a.m. — 12:00 p.m.
KK 1121
Professor Linbo Wang
Learning from Online Ratings
About Speaker
Prof. Xiang Hui

Assistant Professor of Marketing | OLIN Business School | Washington University in St.Louis

Learning from Online Ratings
31 May, 202410:30 a.m. - 12:00 p.m.
KK 910, K. K. Leung Building, HKU
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