Posterior-Predictive Evidence on US Inflation Using Extended New Keynesian Phillips Curve Models with Non-Filtered Data
Posterior-Predictive Evidence on US Inflation Using Extended New Keynesian Phillips Curve Models with Non-Filtered Data
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使用扩展的新凯恩斯菲利普斯曲线模型和未过滤数据对美国通货膨胀的后预测证据
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发表时间：
2013
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通讯作者：
H. V. Dijk
中科院分区：
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作者：
N. Basturk;C. Çakmaklı;Pinar Ceyhan;H. V. Dijk
以边际成本衡量的美国通胀和经济活动不断变化的时间序列属性，在一组扩展的菲利普斯曲线(PC)模型中进行建模。结果表明，机械去除或模拟数据中的简单低频运动可能会产生较差的预测结果，这取决于所使用的模型规格。基本的PC模型被扩展到包括结构时间序列模型，该模型描述了水平和波动性中的典型时变模式。通货膨胀的前瞻性和回溯性预期成分被纳入，并对它们的相对重要性进行了评估。引入预期通胀的调查数据是为了强化可能性中的信息。利用基于模拟的贝叶斯技术进行实证分析。关于通胀和边际成本之间的内生性和长期稳定性，没有找到可信的证据。向后看的通胀似乎比向后看的通胀更强。使用富PC模型可以更准确地估计通胀水平和波动性。扩展的PC结构在拟合和预测方面优于现有的基本贝叶斯向量自回归模型和随机波动率模型。完全预测分布的尾部表明，近年来通货紧缩的可能性有所增加。
Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended Phillips Curve (PC) models. It is shown that mechanical removal or modeling of simple low frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic PC models are extended to include structural time series models that describe typical time varying patterns in levels and volatilities. Forward and backward looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking one. Levels and volatilities of inflation are estimated more precisely using rich PC models. The extended PC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years.