Abstract
More and more clues show that the bidders tend to risk averse. However, traditional nonparametric approach is only applicable for the case of risk neutrality. This study proposes a generalized nonparametric structural estimation procedure for the first-price auctions. To evaluate the performance, extensive Monte Carlo simulation experiments are conducted for ten different values of risk aversion parameter including the risk neutrality case in multiple classic scenes. Though there are no unique estimators for risk aversion parameter, four (weighted) combinations of all estimators are obtained, and some guidance is also given for real-world applications. Finally, empirical results on USFS bidding dataset show that the our nonparametric method can capture bidders’ risk aversion to some extend.
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An, X., Chen, J., Zhang, Y. (2014). Risk Aversion Parameter Estimation for First-Price Auction with Nonparametric Method. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_39
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DOI: https://doi.org/10.1007/978-3-642-40675-1_39
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