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Structural Time Series Models for Business Cycle Analysis

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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.

Suggested Citation

  • Tommaso Proietti, 2008. "Structural Time Series Models for Business Cycle Analysis," CEIS Research Paper 109, Tor Vergata University, CEIS, revised 10 Jul 2008.
  • Handle: RePEc:rtv:ceisrp:109
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    6. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    7. Ehrmann, Michael & Smets, Frank, 2003. "Uncertain potential output: implications for monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1611-1638, July.
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    Cited by:

    1. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    2. Andrle, Michal, 2008. "The Role of Trends and Detrending in DSGE Models," MPRA Paper 13289, University Library of Munich, Germany.
    3. Hjelm, Göran & Jönsson, Kristian, 2010. "In Search of a Method for Measuring the Output Gap of the Swedish Economy," Working Papers 115, National Institute of Economic Research.
    4. Fabio Busetti & Michele Caivano, 2013. "The trend-cycle decomposition of output and the Phillips curve: Bayesian estimates for Italy," Temi di discussione (Economic working papers) 941, Bank of Italy, Economic Research and International Relations Area.
    5. Winkelried, Diego, 2013. "Modelo de Proyección Trimestral del BCRP: Actualización y novedades," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 26, pages 9-60.
    6. Siliverstovs Boriss, 2013. "Dating Business Cycles in Historical Perspective: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 661-679, October.
    7. Ciccarelli, Carlo & Fenoaltea, Stefano & Proietti, Tommaso, 2008. "The comovements of construction in Italy's regions, 1861-1913," MPRA Paper 8870, University Library of Munich, Germany.

    More about this item

    Keywords

    State Space Models. Kalman Filter and Smoother. Bayesian Estimation.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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