The natural interest rate in semi-structural unobserved components models

半结构不可观测成分模型中的自然利率

• 批准号: 437769753
• 负责人: Professor Dr. Tino Berger
• 金额: --
• 依托单位: Lehrstuhl für Außenwirtschaftslehre und angewandte Ökonometrie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/437769753?language=en
• 关键词: natural; interest; rate; semi; structural

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.

我们的项目沿着沿着两条不同的途径推进自然利率（NRI）估计的科学知识前沿。我们将证明，利用结构性多指标未观测成分模型内的横截面数据，导致估计不确定性的大幅减少和估计NRI的精度的显着提高。调查的第二个途径是有关的选择和适当的计量经济学模型的因素，确定NRI。我们开发了一个贝叶斯模型选择程序来确定经验的时间序列属性的NRI。我们还建议结合联合收割机的贝弗里奇-纳尔逊趋势周期分解与SVAR方法，以确定NRI及其潜在的结构决定因素。