Structural Time Series Models for Business Cycle Analysis

45 Pages Posted: 1 Apr 2008 Last revised: 24 Feb 2014

See all articles by Tommaso Proietti

Tommaso Proietti

University of Rome II - Department of Economics and Finance

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

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

Tommaso Proietti (Contact Author)

University of Rome II - Department of Economics and Finance ( email )

Via Columbia, 2
Rome, 00133
Italy

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