Nonparametric Identification and Inference of First-Price Auctions with Heterogeneous Bidders
Bibliographic Data
| ID | 19418888 |
|---|---|
| Authors | Zheng Li (0000-0002-7537-2509, North Carolina State University, corresponding author) |
| Year | 2024 |
| Volume | 42 |
| Issue | 4 |
| Pages | 1185-1194 |
| Publication date | 2024-10-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2023.2299432 |
| OpenAlex | W4390266910 |
| Language | EN |
| References cited | 32 |
In the auction literature, it is well established that bidders’ asymmetry plays an important role in determining auction revenues. In this article, we propose a nonparametric methodology to analyze first-price auctions with two popularly adopted asymmetries: heterogeneous risk preferences and asymmetric value distributions. We find that the two competing models provide distinct implications for the bid distributions conditional on heterogeneity. By modeling bidders’ asymmetry as unobserved heterogeneity, we show that the conditional bid distributions are identified nonparametrically. These results enable researchers to test the two competing models. In an application using the US Forest Service timber auction data, we find that the data supports the model with heterogeneity in risk preference
Common value auction · Econometrics · Economics · Inference · Microeconomics · Nonparametric statistics · Auction Theory and Applications · Computer Science · Consumer Market Behavior and Pricing · Supply Chain and Inventory Management · Artificial Intelligence
| Highly cited | No |
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