The main objective of this paper is to equip the trade policy analyst with an appropriate method of seasonally adjusting trade data with weekly observations. To that end, a structural time series model containing a trend, seasonal and irregular component is specified. The seasonal component is represented by a time-varying periodic spline. Casting the model in state-space form enables time-varying parameters as well as use of the powerful Kalman filter for trend estimation. The resulting trend can then be interpreted as a seasonally adjusted series. A simulation exercise shows that the correct trend is identified with an average absolute error of 0.4 percent. An application to Swedish imports during 2017-2021 shows that the model produces a reasonable trend estimate when applied in 'real-time' and that its application is preferred to smoothing the series using a simple moving average.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-445075 |
| Date | January 2021 |
| Creators | Jägerstedt, Hannes |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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