The natural interest rate in semi-structural unobserved components models

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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.