Risk aversion and asymmetry in procurement auctions: Identification, estimation and application to construction procurements
采购拍卖中的风险厌恶与非对称性:识别、估计及建筑采购应用AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要通过采购拍卖的出价限制识别风险偏好和潜在成本分布,直接属于结构估计,风险厌恶并非金融尾部风险指标。
采购拍卖中的风险厌恶与非对称性:识别、估计及建筑采购应用AI 解读 ↓
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This article studies a model of asymmetric risk averse bidding within the independent private value paradigm. The inherent asymmetry in cost and risk aversion imposes an original restriction on the observed bid data, an exact equality which leads to the model semiparametric identification and estimation. The unobserved arguments of this equality need to be simulated in order to estimate the bidders’ Constant Relative Risk Aversion or Constant Absolute Risk Aversion parameters and their heterogeneous cost distributions. In the Los Angeles City Hall construction contracts offered between 1994 and 2003, the model and methodology help reveal that financial asymmetries affect the firms’ cost distribution, while experience influences their degree of risk aversion.
BLP 模型中的条件矩估计与推断
The random-coefficient demand model of Berry, Levinsohn, and Pakes (1995) is commonly estimated by the generalized method of moments (GMM), using an unconditional moment restriction with a fixed set of instruments. Identification of the model, however, rests on a conditional moment restriction. The two are not equivalent: the unconditional restriction may admit additional parameter values. We construct a counterexample in which the model is identified by the conditional…
产品内生进入与退出下需求模型的识别和估计
Firms introduce products in markets where they anticipate stronger demand, using information unobserved by researchers. This creates endogenous selection in demand estimation. In differentiated-product oligopolies, multidimensional demand unobservables and strategic entry can render the ordinary propensity score insufficient for selection correction. Existing approaches either restrict firms' information at entry or jointly estimate demand, pricing, and entry under strong…
基于机器学习的去偏 U 统计量
We propose a method to debias estimators based on U-statistics with machine-learning (ML) first steps. Standard plug-in estimators often suffer from regularization and model-selection biases, leading to invalid inference. We characterize orthogonal adjustment terms for two-step U-statistics, construct Debiased Machine Learning (DML) U-estimators, develop a cross-fitting algorithm, and establish a general asymptotic theory for inference with ML first steps. We illustrate the…
相互依赖的首次到达时间
This paper studies interdependent durations as equilibrium outcomes of a synchronization game, a continuous-time stopping game in which the incentive to stop increases when other players stop. We allow the payoffs to vary with both common shocks and observed and unobserved agent characteristics. The common shocks follow a spectrally negative Lévy process, a semiparametric process that includes Brownian motion as a special case but may also have jumps. We show that…
非线性结构模型中边际效应分布与分位数的面板估计:垃圾食品需求应用
In this paper, we propose a framework to estimate the distribution of marginal effects in a general class of structural models that allow for very general nonlinearities, high dimensional heterogeneity, and unrestricted correlation between the persistent components of this heterogeneity and all covariates. The main idea is to form a derivative dependent variable using two periods of the panel and use differences in outcome variables of nearby subpopulations to obtain the…
半参数多层样本选择模型的识别与估计
Many selection problems are multilayered: agents first decide whether to participate and then sort among ordered or unordered categories. This paper shows that the sorting layer changes the geometry of identification. Unlike binary selection, in which selection bias can be summarized by a scalar control function, ordered and multinomial sorting generally produce multi-index control functions whose dimension determines the continuous covariate variation needed for…