We consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood‐based estimators in mean squared error and composite models are superior to individual models in the Kullback–Leibler sense. We describe Bayesian quasi‐posterior computations and compare our approach to Bayesian model averaging, finite mixture, and robust control procedures. We robustify inference using the composite posterior distribution of the parameters and the pool of models. We provide estimates of the marginal propensity to consume and evaluate the role of technology shocks for output fluctuations.

中文翻译：

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处理结构宏观计量经济学模型中的错误指定

我们考虑了一组潜在错误指定的结构模型，在几何上组合了它们的似然函数，并使用复合方法估算了参数。在蒙特卡洛研究中，在均方误差中，复合估计量主导基于似然的估计量，并且在Kullback-Leibler的意义上，复合模型优于单个模型。我们描述贝叶斯拟后验计算，并将我们的方法与贝叶斯模型平均，有限混合和鲁棒控制程序进行比较。我们使用参数和模型池的复合后验分布来增强推理能力。我们提供了消费边际倾向的估计，并评估了技术冲击对产出波动的作用。