We investigate the evidence for structural breaks in autoregressive models of U.S. macroeconomic time series. There is substantial model uncertainty associated with such models, including uncertainty related to lag selection, the number of structural breaks, and the specific parameters that break. We develop a feasible approach to Bayesian model averaging, where the model space encompasses these sources of uncertainty. We find pervasive evidence for breaks in variance parameters, and for price inflation series, we find strong evidence of changes in persistence. We also find evidence for reductions in trend growth rates of production series. For most series, there is substantial model uncertainty, calling into question the common practice of basing inference on one selected structural break model.

中文翻译：

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美国宏观经济时间序列的结构性断裂：贝叶斯模型平均方法

我们调查了美国宏观经济时间序列自回归模型中结构性断裂的证据。此类模型存在大量模型不确定性，包括与滞后选择相关的不确定性、结构断裂的数量以及断裂的具体参数。我们开发了一种可行的贝叶斯模型平均方法，其中模型空间包含这些不确定性来源。我们发现了方差参数中断的普遍证据，对于价格通胀序列，我们发现了持久性变化的有力证据。我们还发现了生产系列趋势增长率降低的证据。对于大多数系列，存在大量的模型不确定性，这对基于一个选定的结构断裂模型进行推理的普遍做法提出了质疑。