Forecasting Using Relative Entropy
提出一种相对熵方法，通过最小化Kullback-Leibler信息准则，在模拟预测分布中施加矩约束，生成满足约束的新分布，并举例说明如何引入其他预测或经济理论的约束。
Abstract: The paper describes a relative entropy procedure for imposing moment restrictions on simulated forecast distributions from a variety of models. Starting from an empirical forecast distribution for some variables of interest, the technique generates a new empirical distribution that satisfies a set of moment restrictions. The new distribution is chosen to be as close as possible to the original in the sense of minimizing the associated Kullback-Leibler Information Criterion, or relative entropy. The authors illustrate the technique by using several examples that show how restrictions from other forecasts and from economic theory may be introduced into a model’s forecasts.