This paper studies the joint dynamics of U.S. inflation and a term structure of average inflation predictions taken from the Survey of Professional Forecasters (SPF). We estimate these joint dynamics by combining an unobserved components (UC) model of inflation and a sticky‐information forecast mechanism. The UC model decomposes inflation into trend and gap components, and innovations to trend and gap inflation are affected by stochastic volatility. A novelty of our model is to allow for time‐variation in inflation‐gap persistence as well as in the frequency of forecast updating under sticky information. The model is estimated with sequential Monte Carlo methods that include a particle learning filter and a Rao–Blackwellized particle smoother. Based on data from 1968Q4 to 2018Q3, estimates show that (i) longer horizon average SPF inflation predictions inform estimates of trend inflation; (ii) inflation gap persistence is countercyclical before the Volcker disinflation and acyclical afterwards; (iii) by 1990 sticky‐information inflation forecast updating is less frequent than it was earlier in the sample; and (iv) the drop in the frequency of the sticky‐information forecast updating occurs at the same time persistent shocks become less important for explaining movements in inflation. Our findings support the view that stickiness in survey forecasts is not invariant to the inflation process.

中文翻译：

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通货膨胀和专业预测动态：粘性，持久性和波动性的评估

本文研究了美国通胀的联合动态以及从专业预测者调查（SPF）中获得的平均通胀预测的期限结构。我们通过结合未观察到的通货膨胀（UC）模型和粘性信息预测机制来估计这些联合动力。UC模型将通货膨胀分解为趋势和缺口通货膨胀，而趋势和缺口通货膨胀的创新受到随机波动的影响。我们模型的新颖性在于允许通货膨胀缺口持续时间随时间变化以及粘性信息下的预测更新频率。该模型是使用包括粒子学习滤波器和Rao-Blackwellized粒子平滑器的顺序蒙特卡洛方法进行估计的。基于数据从1968 Q 4至2018 Q3，估计表明：（i）较长的平均SPF通胀预测有助于趋势通胀的估计；（ii）通货膨胀缺口的持久性在沃尔克通货紧缩之前是反周期的，而在其后是非周期性的；（iii）到1990年，粘滞信息通货膨胀预测的更新频率比样本中的早期数据少；（iv）粘性信息预测更新频率下降的同时，持续的冲击对于解释通货膨胀的影响变得不那么重要。我们的研究结果支持这样的观点，即调查预测中的粘性不会随通货膨胀过程而变化。