Nonparametric estimation of English auctions with selective entry: An application to online judicial auctions

Abstract This paper proposes an estimation approach following the constructive identification strategy of Athey and Haile, and Gentry and Li with adaption in the context of ascending auctions with selective entry. Our estimators are shown to be consistent in a large sample and to perform well in a finite sample by a simulation study. We apply our estimation approach to the Alibaba online judicial auctions of used cars to recover the bounds of conditional value distribution and the entry cost. The bounds estimates of both conditional value distribution and entry cost are quite tight (resp., relatively wide) for middle‐valued (resp., low‐valued or high‐valued) signal, and the cumulative distribution functions of conditional value distribution given signal comply with the law of ordered dominance. Finally, our counterfactual analysis indicates that (i) the ascending auction yields a higher revenue than the first‐price sealed bid auction, and (ii) the revenue can be improved significantly when the entry cost is cut by half

A Nonparametric Test of Exogenous Participation in First-Price Auctions *

Abstract This paper proposes a nonparametric test of exogenous participation in first-price auctions. Exogenous participation means that the valuation distribution does not depend on the number of bidders. Our test is motivated by the fact that two valuation distributions are the same if and only if their generalized Lorenz curves are the same. Our method avoids estimating unobserved valuations and does not require smooth estimation of bid density. We show that our test is consistent against all fixed alternatives and has power against root-n local alternatives. Monte Carlo experiments show that our test performs well in finite samples. We implement our method on data from the U.S. Forest Service timber auctions. We also discuss how our test can be adapted to other testing problems in auctions

Analysis on Impact of Land Use Change on Urban Waterlogging Caused by Floods

In recent years, ultra-high-intensity rainfall at home and abroad has caused frequent urban waterlogging disasters, posing a severe threat to people’s lives, property and city’s safety. Based on the satellite image data of Shanghai Waigaoqiao Free Trade Zone in different periods and the Storm Water Management Model (SWMM), this paper establishes a model of heavy rainfall under the underlying surface of a complex city, and analyses topographic features, different land use types, rainfall infiltration intensity and the characteristics of the drainage pipe network. The rainwater accumulation under different rainstorms and urbanization levels is simulated and analysed. The research results show that urban rainstorm accumulation is closely related to land use changes. With the increase of surface impermeability and rainfall intensity, the risk of waterlogging in the study area tends to increase: From 1994 to 2019, the construction area has increased from 2.5096km2 to 5.8662km2 in the study area. Compared with 1994, under the same rainfall conditions, the simulated flooding node and runoff coefficient in 2019 both increased significantly

Multi-dimensional dynamic simulation of rainstorm waterlogging in urban communities

One major threat to cities at present is the increasing rainstorm waterlogging hazards due to climate change and accelerated urbanization. This paper explores the mechanism of rainstorm waterlogging and enables the fine simulation of surface water propagation over complex urban terrain. A novel community-scale waterlogging modeling scheme is presented by loosely coupling a one-dimensional sewer model with a two-dimensional overland model under an open-source framework. The coupled model was applied to Waigaoqiao Free Trade Zone located in Pudong New Area of Shanghai. To quantify the influence of rainfall intensity and drainage conditions on the waterlogging, 12 scenarios were constructed by combining four rainfall return periods (3, 5, 10, and 20 a) and three startup water depths (1.5, 2.0, and 2.5 m) of pump stations. The multi-scenario simulation results show that the waterlogging risk increases from north to south in the study area, and that risk zones with water depth above 0.3 m are mostly concentrated in the southwest and southeast corners of the site. The longer the rainfall return period, the larger the submerged area, and the spatial distribution of surface water accumulation is affected by local topography and drainage system. In addition, reducing the startup water depth of pump stations has an obvious effect on inhibiting the severity of water accumulation. The results provide insights into overland flow across an urban area with densely populated buildings and help to reduce the risk of rainstorm-induced waterlogging disasters