Structural Time Series Models for Business Cycle Analysis

Tommaso Proietti

No 109, CEIS Research Paper from Tor Vergata University, CEIS

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. (search for similar items in EconPapers)
Pages: 45 pages
Date: 2008-07-10, Revised 2008-07-10
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Citations: View citations in EconPapers (4)

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Related works:
Chapter: Structural Time Series Models for Business Cycle Analysis (2009)
Working Paper: Structural Time Series Models for Business Cycle Analysis (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:rtv:ceisrp:109

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