Arbeitspapier

Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment

This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic trend, seasonal and cycle components. Convenient state space formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues are considered and a novel periodic version of the stochastic cycle component is presented. In the empirical illustration, the model is applied to postwar monthly US unemployment series and we discover a significantly periodic cycle. Furthermore, a comparison is made between the performance of the periodic unobserved components time series model and a periodic seasonal autoregressive integrated moving average model. Moreover, we introduce a new method to estimate the latter model.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. 06-101/4

Klassifikation
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Thema
Unobserved component models
state space methods
seasonal adjustment
time–varying parameters
forecasting

Ereignis
Geistige Schöpfung
(wer)
Koopman, Siem Jan
Ooms, Marius
Hindrayanto, Irma
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2006

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Koopman, Siem Jan
  • Ooms, Marius
  • Hindrayanto, Irma
  • Tinbergen Institute

Entstanden

  • 2006

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