Nonparametric estimation of first price auctions via density–quantile function

Yu Yvette Zhang

Economics Letters, 2022, vol. 216, issue C

Abstract: We propose a nonparametric estimator of bidders’ value function based on a kernel estimator of the density–quantile function of bids in first price auctions. This estimator provides certain advantage over the conventional approach that relies on the distribution/density ratio of the bids. We use a boundary-adaptive kernel for boundary bias correction and propose a practical method of bandwidth selection. Our numerical experiments demonstrate good performance of the proposed method in the estimation of first price auctions with risk neutral and risk averse bidders.

Keywords: Density–quantile function; First price auctions; Kernel smoothing; Bandwidth selection; Boundary bias correction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:216:y:2022:i:c:s0165176522001604

DOI: 10.1016/j.econlet.2022.110560

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