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Structural Time Series Models for Business Cycle Analysis

Tommaso Proietti
University of Rome II - Dipartimento S.E.F. e Me.Q.

January 2008

CEIS Research Paper No. 109

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 Classifications: C32, E32, C22

Working Paper Series

Date posted: April 01, 2008 ; Last revised: May 25, 2008

Suggested Citation

Proietti, Tommaso, Structural Time Series Models for Business Cycle Analysis (January 2008). CEIS Research Paper No. 109. Available at SSRN: http://ssrn.com/abstract=1114854

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Contact Information

Tommaso Proietti (Contact Author)

University of Rome II - Dipartimento S.E.F. e Me.Q. ( email )

Via Columbia n.2
I-00133 Rome Italy