This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (2013a). The Bernoulli model, however, is unable to account for model change over time, which is important if the set of relevant predictors changes over time. To tackle this problem, this paper extends the Bernoulli model by introducing a novel modeling approach called Markov Dimension Switching (MDS). MDS allows the set of predictors to change over time. The MDS and Bernoulli model reveal that the unemployment rate, the Treasury bill rate and the number of newly built houses are the most important variables in the generalized Phillips curve. Furthermore, these three predictors exhibit a sizeable degree of time variation for which the Bernoulli approach is not able to account, stressing the importance and benefit of the MDS approach. In a forecasting exercise the MDS model compares favorably to the Bernoulli model for one quarter and one year ahead inflation. In addition, it turns out that the performance of MDS model forecasting is competitive in comparison with other models found to be useful in the inflation forecasting literature.

中文翻译：

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使用马尔可夫维度转换预测美国通货膨胀*

本研究使用 Korobilis (2013a) 的伯努利方法在菲利普斯曲线环境中考虑贝叶斯变量选择。然而，伯努利模型无法解释模型随时间的变化，如果相关预测变量集随时间变化，这一点很重要。为了解决这个问题，本文通过引入一种称为马尔可夫维度切换 (MDS) 的新型建模方法来扩展伯努利模型。MDS 允许预测变量集随时间变化。MDS 和伯努利模型显示失业率、国库券利率和新建房屋数量是广义菲利普斯曲线中最重要的变量。此外，这三个预测变量表现出相当大的时间变化程度，这是伯努利方法无法解释的，强调 MDS 方法的重要性和好处。在预测练习中，MDS 模型在四分之一和一年的通胀前优于伯努利模型。此外，事实证明，与在通货膨胀预测文献中发现有用的其他模型相比，MDS 模型预测的性能具有竞争力。