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信息获取与投标准备：木材拍卖进入和竞价的结构分析AI 解读

Recently, several auction models with entry have been proposed: in one model (Levin and Smith, 1994; Li and Zheng, 2009), bidders are assumed to draw their private values after they decide to enter. In another model (Samuelson, 1985; Li and Zheng, 2009), bidders are assumed to learn their values before their entry decisions are made. The entry cost in the latter model can be interpreted as bid preparation cost, while the entry cost in the former model consists of both costs…

计件锦标赛中的同质与异质参赛者：理论与实证分析AI 解读

In this article we show that sorting different ability contestants in piece rate tournaments into more homogenous groups alters agents’ incentives to exert effort. We propose a method for structurally estimating the piece rate tournament game with heterogenous players and apply it to the payroll data from a broiler production contract. Our counterfactual analysis shows that under reasonable assumptions, both the principal and the growers can gain when the tournament groups…

第一价格拍卖中的进入与竞争效应：采购拍卖的理论和证据AI 解读

Motivated by several interesting features of the highway mowing auction data from the Texas Department of Transportation (TDoT), we study three competing procurement auction models with endogenous entry. Our entry and bidding models provide several interesting implications. For the first time, we show that even within an independent private value paradigm, as the number of potential bidders increases, bidders' equilibrium bidding behaviour can become less aggressive, and the…

含不可观测异质性的动态 Tobit 面板模型：半参数贝叶斯推断AI 解读

Abstract This paper develops semiparametric Bayesian methods for inference of dynamic Tobit panel data models. Our approach requires that the conditional mean dependence of the unobserved heterogeneity on the initial conditions and the strictly exogenous variables be specified. Important quantities of economic interest such as the average partial effect and average transition probabilities can be readily obtained as a by‐product of the Markov chain Monte Carlo run. We apply…