Lutero, Giancarlo and Piovani, Alessandro (2025): Several seasonal adjustment strategies in problematic contexts.
PDF
MPRA_paper_123386.pdf Download (2MB) |
Abstract
The past few years have been marked by the occurrence of many unexpected events that have had many social and economic repercussions, with the COVID-19 pandemic and rising tensions in energy commodity markets standing out above the others. This period of great uncertainty has also had a considerable effect on the production of official economic statistics, undermining the goodness and the predictive capacity of short-term stochastic models. In this condition of extreme unpredictability, there is a need for a strategy of monitoring and reviewing the seasonal adjustment models and anomalous observations, especially over the period 2020-2023. In this work several intervention strategies were defined and tested, focusing over series that manifested a distinct break in their dynamic. Temporary level shifts, included with their lagged versions, have proven to be a particularly useful tool. The outcomes reveal that the policies we considered are effective, and the TRAMO-SEATS procedure manages to be helpful in both ordinary and extraordinary conditions. The whole data analysis has been conducted with JDemetra+ that is a complete and flexible tool in performing several statistical estimates and tests.
Item Type: | MPRA Paper |
---|---|
Original Title: | Several seasonal adjustment strategies in problematic contexts |
Language: | English |
Keywords: | Seasonal adjustment, structural breaks, outlier detection, intervention variables, JDemetra+ |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection |
Item ID: | 123386 |
Depositing User: | Alessandro Piovani |
Date Deposited: | 22 Jan 2025 14:40 |
Last Modified: | 22 Jan 2025 14:40 |
References: | Eurostat. Guidance on time series treatment in the context of the Covid-19 crisis, 2020. U.S. Bureau of the Census. Seasonal adjustment of time series during the pandemic. https://www2.census.gov/about/partners/cac/sac/meetings/2022-03/presentation-seasonal-adjustment-of-time-series-during-pandemic.pdf, 2022. Accessed: 2025-01-15. Victor Gomez and Agustin Maravall. Programs tramo and seats. instructions for the user (with some updates). Technical report, Working paper, 1996. Vıctor Gomez and Agustın Maravall Herrero. Seasonal adjustment and signal extraction in economic time series. Banco de Espa˜na. Servicio de Estudios, 1998. Estela Bee Dagum. The X-II-ARIMA seasonal adjustment method. Statistics Canada, 1980. Estela Bee Dagum. Modelling, forecasting and seasonally adjusting economic time series with the x-11 arima method. Journal of the Royal Statistical Society. Series D (The Statistician), 27(3/4):203–216, 1978. U.S. Bureau of the Census. X-13arima-seats reference manual. Technical report, Time Series Research Staff, Statistical Research Division, 2015. Sylwia Grudkowska, Dario Buono, Jean Palate, and Wojciech Ciebiera. Advanced tools for time series analysis and seasonal adjustment in the new jdemetra+. JSM Proceedings Paper, 2013. S. Grudkowska. JDemetra+ reference manual version 1.1, 2015. S. Grudkowska. JDemetra+ User Guide, 2015. Anna Smyk and Alice Tchang. R tools for jdemetra+ seasonal adjustment made easier. Technical report, Institut National de la Statistique et des Etudes Economiques, 2021. Jean Palate. Jdemetra+, an open framework for seasonal adjustment. 2013. J Peter Burman. Seasonal adjustment by signal extraction. Journal of the Royal Statistical Society Series A: Statistics in Society, 143(3):321–337, 1980. European Commission and Eurostat. ESS guidelines on seasonal adjustment– 2015 edition. Publications Office, 2015. Graham Elliott, Clive Granger, and Allan Timmermann, editors. Handbookof Economic Forecasting, volume 1. Elsevier, 1 edition, 2006. George EP Box and George C Tiao. Intervention analysis with applicationsto economic and environmental problems. Journal of the American Statistical association, 70(349):70–79, 1975. Patrick Foley. Seasonal adjustment of irish official statistics during the covid-19 crisis. Statistical Journal of the IAOS, 37(1):57–66, 2021. Victor Gomez and Agustin Maravall. Automatic modeling methods for univariate series. A course in time series analysis, pages 171–201, 2001. Chung Chen and Lon-Mu Liu. Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88(421):284–297, 1993. Edward J Hannan and Jorma Rissanen. Recursive estimation of mixed autoregressive-moving average order. Biometrika, 69(1):81–94, 1982. Agustin Maravall. Unobserved components in economic time series. Handbook of Applied Econometrics Volume 1: Macroeconomics, pages 1–51, 1999. A Maravall and G Caporello. Program tsw: Revised reference manual, 2004. Nassim Nicholas. The black swan: the impact of the highly improbable. Journal of the Management Training Institut, 36(3):56, 2008. David F Hendry and Grayham E Mizon. Unpredictability in economic analysis, econometric modeling |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/123386 |
Available Versions of this Item
-
Several seasonal adjustment strategies in problematic contexts. (deposited 21 Jan 2025 14:49)
- Several seasonal adjustment strategies in problematic contexts. (deposited 22 Jan 2025 14:40) [Currently Displayed]