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Are Central Bankers Inflation Nutters? - A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model

机译:央行行长是通货紧缩吗? -状态空间模型中长记忆参数的贝叶斯MCMC估计器

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摘要

Several central banks have adopted inflation targets. The implementation of these targets is flexible; thecentral banks aim to meet the target over the long term but allow inflation to deviate from the target in the short-termin order to avoid unnecessary volatility in the real economy. In this paper, we propose modeling the degree offlexibility using an ARFIMA model. Under the assumption that the central bankers control the long-run inflationrates, the fractional integration order captures the flexibility of the inflation targets. A higher integration order isassociated with a more flexible target. Several estimators of the fractional integration order have been proposed inthe literature. Grassi and Magistris (2011) show that a state-based maximum likelihood estimator is superior to otherestimators, but our simulations show that their finding is over-biased for a nearly non-stationary time series. Weresolve this issue by using a Bayesian Monte Carlo Markov Chain (MCMC) estimator. Applying this estimator toinflation from six inflation-targeting countries for the period 1999M1 to 2013M3, we find that inflation is integratedof order 0.8 to 0.9 depending on the country. The inflation targets are thus implemented with a high degree offlexibility.
机译:几家中央银行已经采用了通胀目标。这些目标的执行是灵活的;中央银行的目标是长期实现目标,但允许通货膨胀在短期内偏离目标,以避免实体经济不必要的波动。在本文中,我们建议使用ARFIMA模型对柔性度进行建模。在中央银行控制长期通货膨胀率的假设下,分数积分阶数体现了通货膨胀目标的灵活性。集成顺序越高,目标就越灵活。文献中已经提出了分数积分阶数的几种估计。 Grassi和Magistris(2011)表明,基于状态的最大似然估计器优于其他估计器,但我们的模拟表明,他们的发现在几乎非平稳的时间序列上存在偏见。通过使用贝叶斯蒙特卡洛马尔可夫链(MCMC)估计器解决了此问题。将这个估计量应用于六个以通胀为目标的国家在1999M1到2013M3期间的通货膨胀,我们发现根据国家的不同,通货膨胀率在0.8到0.9之间。因此,以高度的灵活性实现充气目标。

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