A Composite Likelihood Approach for Dynamic Structural Models
解释如何利用复合似然函数改善动态随机一般均衡模型的估计、计算和推断问题，通过整合不同模型或数据集的信息来估计共同参数，并举例说明该方法的应用和性质。
Abstract 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.