Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US in.ation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

时变参数(TVP)模型在实证宏观经济学中越来越受欢迎。然而，TVP模型是参数丰富和风险过拟合的，除非模型的维数很小。出于这种担忧，本文提出了几个时变维(TVD)模型，其中模型的维数可以随时间变化，允许模型自动选择更节省的TVP表示，或者在不同的节省表示之间切换。我们的TVD模型都属于动态混合模型。讨论了这些模型的性质，并给出了贝叶斯推理的方法。一个我们参与的应用程序。预测对不同的TVD模型进行了说明和比较。我们发现我们的TVD方法比几个标准基准表现出更好的预测性能，并缩小到更节省的规格。