The natural interest rate in semi-structural unobserved components models

我们的项目沿着两条不同的道路前进，在估计自然利率（NRI）方面推进科学知识的前沿。我们将证明，利用结构多指标未观察成分模型中的横截面数据，可以大大减少估计不确定性，并显著提高估计NRI的精度。第二个调查途径是关于NRI决定因素的选择和适当的计量经济模型。我们开发了一个贝叶斯模型选择程序来确定NRI的经验时间序列特性。我们还建议将贝弗里奇-尼尔森趋势周期分解与SVAR方法相结合，以确定NRI及其潜在的结构决定因素。

Our project moves along two different avenues of advancing the frontier of scientific knowledge in estimating the natural rate of interest (NRI). We are going to demonstrate that utilizing cross-sectional data within a structural multiple-indicator unobserved components model leads to a substantial reduction in estimation uncertainty and a dramatic improvement in the precision of estimating the NRI. The second avenue of investigation is concerned with the selection and appropriate econometric modeling of the factors determining the NRI. We develop a Bayesian model selection procedure to determine empirically the time series properties of the NRI. We also propose to combine a Beveridge-Nelson trend-cycle decomposition with a SVAR approach to identify the NRI and its underlying structural determinants.