Structural Time Series Models for Business Cycle Analysis
45 Pages Posted: 1 Apr 2008 Last revised: 24 Feb 2014
Date Written: January 2008
Abstract
The chapter deals with parametric models for the measurement of the business cycle in economic time series. It presents univariate methods based on parametric trend - cycle decompositions and multivariate models featuring a Phillips type relationship between the output gap and inflation and the estimation of the gap using mixed frequency data. We finally address the issue of assessing the accuracy of the output gap estimates.
Keywords: State Space Models, Kalman Filter and Smoother, Bayesian Estimation
JEL Classification: C32, E32, C22
Suggested Citation: Suggested Citation
Proietti, Tommaso, Structural Time Series Models for Business Cycle Analysis (January 2008). CEIS Research Paper No. 109, Available at SSRN: https://ssrn.com/abstract=1114854 or http://dx.doi.org/10.2139/ssrn.1114854
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