Forecasting inflation with gradual regime shifts and exogenous information-...

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英文文献：Forecasting inflation with gradual regime shifts and exogenous information-利用渐进式变化和外源性信息预测通货膨胀
英文文献作者：Andrés González,Kirstin Hubrich,Timo Ter?svirta
英文文献摘要：
In this work, we make use of the shifting-mean autoregressive model which is a flexible univariate nonstationary model. It is suitable for describing characteristic features in inflation series as well as for medium-term forecasting. With this model we decompose the inflation process into a slowly moving nonstationary component and dynamic short-run fluctuations around it. We fit the model to the monthly euro area, UK and US inflation series. An important feature of our model is that it provides a way of combining the information in the sample and the a priori information about the quantity to be forecast to form a single inflation forecast. We show, both theoretically and by simulations, how this is done by using the penalised likelihood in the estimation of model parameters. In forecasting inflation, the central bank inflation target, if it exists, is a natural example of such prior information. We further illustrate the application of our method by an ex post forecasting experiment for euro area and UK inflation. We find that that taking the exogenous information into account does improve the forecast accuracy compared to that of a linear autoregressive benchmark model.

在本研究中，我们使用位移均值自回归模型，这是一个灵活的单变量非平稳模型。它既适用于通货膨胀序列的特征描述，也适用于中期预测。利用这个模型，我们将膨胀过程分解为一个缓慢移动的非平稳分量和它周围的动态短期波动。我们将该模型应用于欧元区、英国和美国的月度通胀系列。我们的模型的一个重要特征是，它提供了一种方法，将样本中的信息和预测数量的先验信息结合起来，形成一个单一的通货膨胀预测。我们从理论和仿真两方面展示了如何通过在模型参数估计中使用惩罚似然来实现这一点。在预测通胀时，央行的通胀目标(如果存在的话)是此类先验信息的一个自然例子。通过对欧元区和英国通胀的事后预测实验，我们进一步说明了我们的方法的应用。我们发现，与线性自回归基准模型相比，考虑外生信息确实提高了预测的准确性。

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