Arbeitspapier
Seasonality with Trend and Cycle Interactions in Unobserved Components Models
Unobserved components time series models decompose a time series into a trend, a season, a cycle, an irregular disturbance, and possibly other components. These models have been successfully applied to many economic time series. The standard assumption of a linear model, often appropriate after a logarithmic transformation of the data, facilitates estimation, testing, forecasting and interpretation. However, in some settings the linear-additive framework may be too restrictive. In this paper, we formulate a non-linear unobserved components time series model which allows interactions between the trend-cycle component and the seasonal component. The resulting model is cast into a non-linear state space form and estimated by the extended Kalman filter, adapted for models with diffuse initial conditions. We apply our model to UK travel data and US unemployment and production series, and show that it can capture increasing seasonal variation and cycle dependent seasonal fluctuations.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 08-028/4
- Klassifikation
-
Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Seasonal interaction
Unobserved components
Non-linear state space models
Saisonbereinigung
Nichtlineares Verfahren
Zustandsraummodell
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Koopman, Siem Jan
Lee, Kai Ming
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Koopman, Siem Jan
- Lee, Kai Ming
- Tinbergen Institute
Entstanden
- 2008
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