We explain how to use the composite likelihood function to ameliorate estimation, computational and inferential problems in dynamic stochastic general equilibrium models. We combine the information present in different models or data sets to estimate the parameters common across models. We provide intuition for why the methodology works and alternative interpretations of the estimators we construct and of the statistics we employ. We present a number of situations where the methodology has the potential to resolve well-known problems and to provide a justification for existing practices that pool different estimates. In each case, we provide an example to illustrate how the approach works and its properties in practice.

中文翻译：

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动态结构模型的复合似然法

我们解释了如何使用复合似然函数来改善动态随机一般均衡模型中的估计、计算和推理问题。我们结合不同模型或数据集中存在的信息来估计模型间共有的参数。我们提供了该方法为何有效的直觉，以及对我们构建的估计量和我们使用的统计数据的替代解释。我们提出了许多情况，在这些情况下，该方法有可能解决众所周知的问题，并为汇集不同估计的现有做法提供理由。在每种情况下，我们都提供了一个示例来说明该方法的工作原理及其在实践中的特性。