Abstract
Economic time series can be impacted by seasonal factors. If present but not identified, seasonality may lead to incorrect conclusions derived from the analysis. Seasonality is not always easily identifiable as time series are shaped by other factors as well, such as one-off events or natural disasters. There is a variety of methods to deal with seasonality in data. An attempt was made to compare the outcome of two popular methods: X-12-ARIMA and TRAMO/SEATS. They were applied to analyse seasonality for the business climate index in the construction industry in Poland. Both procedures were used to produce a seasonally adjusted series for the business climate index. Comparison of model’s diagnostics proved that TRAMO/SEATS performed slightly better for the analysed series within a chosen time range, which is consistent with some more general results found in the literature.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
