Structural Breaks in U.S. Macroeconomic Time Series: A Bayesian Model Averaging Approach
研究了美国宏观经济时间序列自回归模型中的结构突变证据，开发了一种贝叶斯模型平均方法处理模型不确定性，发现方差参数普遍存在突变，价格通胀序列的持续性有显著变化，生产序列趋势增长率下降。
Abstract 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.