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Optimal Binary Prediction for Group Decision Making

2009; Taylor & Francis; Volume: 28; Issue: 2 Linguagem: Inglês

10.1198/jbes.2009.06120

1537-2707

Robert P. Lieli, Augusto Nieto-Barthaburu,

Forecasting Techniques and Applications

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