Optimal Binary Prediction for Group Decision Making
研究如何为面临不同二元决策问题的异质性群体最优地预测一个二元变量，提出最大福利估计，并推导传统估计量在模型误设时仍具有渐近社会最优性的条件。
Abstract We address the problem of optimally forecasting a binary variable for a heterogeneous group of decision makers facing various (binary) decision problems that are tied together only by the unknown outcome. A typical example is a weather forecaster who needs to estimate the probability of rain tomorrow and then report it to the public. Given a conditional probability model for the outcome of interest (e.g., logit or probit), we introduce the idea of maximum welfare estimation and derive conditions under which traditional estimators, such as maximum likelihood or (nonlinear) least squares, are asymptotically socially optimal even when the underlying model is misspecified. Keywords: : Decision-based forecastingMaximum likelihoodMaximum welfare estimatorMisspecificationMultiple decision makersNonlinear least squares