1. What is seasonal adjustment?
5. Quality of seasonal adjustment
6. Specific issues on seasonal adjustment
Monthly and quarterly time series are often characterised by considerable seasonal variations, which might complicate their interpretation. Such time series are therefore subjected to a process of seasonal adjustment in order to remove the effects of these seasonal fluctuations. Once data have been adjusted for seasonal effects by X-12-ARIMA or some other seasonal adjustment tool, a clearer picture of the time series emerges.
For more information on seasonal adjustment: metadata on methods: seasonal adjustment
The statistics on turnover is part of a system of short-term statistics compiled to monitor the economy. The primary goal of the survey is to monitor the level and development of sales in mining and quarrying, oil and gas extraction, manufacturing, electricity and gas supply.
The turnover will vary through the year because of public holidays. Typically the sales will always be lower in July because of general staff holiday. The different number of working-days in the different months will also influence the pattern of turnover through the year. These circumstances make it difficult to compare the data from month to month. To adjust for these effects the statistics on turnover is seasonally adjusted, and in this way we are able to analyse the underlying development in turnover which says something about the economic cycle from month to month.
For statistics on turnover seasonally adjusted series are published for 32 industry aggregates. These industries are the total, mining and quarrying, oil and gas extraction, manufacturing, electricity supply and different manufacturing industries. I addition seasonally adjusted series aggregated according to Eurostat’s main industrial groupings are published.
Seasonally adjusted series are published for total turnover, export turnover and domestic turnover for each industry aggregate. In total 96 seasonally adjusted series are published.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
• Running an automatic pre-treatment of the raw data based on standard options in the seasonal adjustment tools.
Calendar adjustment involves adjusting for the effects of working days/trading days and for moving holidays. Working days/trading days are adjustment for both the number of working days/trading days and for that the composition of days can vary from one month to another.
• To perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, such as regression or RegARIMA (a regression model with an ARIMA structure for the residuals).
• RegARIMA correction – in this case, the effect of trading days is estimated in a RegArima framework. The effect of trading days can be estimated by using a correction for the length of the month or leap year, regressing the series on the number of working days, etc. In this case, the residuals will have an ARIMA structure.
• Automatic correction. If performed by X-12-ARIMA, automatic correction of raw data will be based on US holidays.
• Use of default calendars. The default in X-12-ARIMA is the US calendar.
Outliers, or extreme values, are abnormal values of the series
• Outliers are detected automatically by the seasonal adjustment tool. The outliers are removed before seasonal adjustment is carried out, and then reintroduced into the seasonally adjusted data.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
• Automatic model selection by established routines in the seasonal adjustment tool.
Comments: Log transformation of the unadjusted figures is carried out
The decomposition scheme specifies how the various components – basically trend-cycle, seasonal and irregular – combine to form the original series. The most frequently used decomposition schemes are the multiplicative, additive or log additive.
• Manual decomposition scheme selection after graphical inspection of the series.
Comments: For most series a multiplicative decomposition is used. The additive method is used for some series with zero-values.
• X-12-ARIMA
In some series, consistency between raw and seasonally adjusted series is imposed.
• Do not apply any constraint.
In some series, consistency between seasonally adjusted totals and the aggregate is imposed .For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.
• Do not apply any constraint.
Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.
• Direct approach where the raw data are aggregated and the aggregates and components are then directly seasonally adjusted using the same approach and software. Any discrepancies across the aggregation structure are not removed.
When performing seasonal adjustment of a time series, it is possible to choose the period to be used in estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the series.
• The whole time series is used to estimate the model and the correction factors
Seasonally adjusted data may change due to a revision of the unadjusted (raw) data or the addition of new data. Such changes are called revisions, and there are several ways to deal with the problem of revisions when publishing the seasonally adjusted statistics.
• Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and release calendar.
• The model, filters, outliers and regression parameters are re-identified and re-estimated continuously as new or revised data become available.
• The revision period for the seasonally adjusted results is limited to 3-4 years (preferably 4) prior to the revision period of the unadjusted data, while older data are frozen.
Comments: The seasonally adjusted figures are updated 4 years back when new data is added.
• Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
• For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.
Table of quality measurement for this statistics
For more information on the quality indicator in the table see: metadata on methods: seasonal adjustment
• All series are sufficiently long to perform an optimal seasonal adjustment.
• Problematic series are treated in a special way only when they are relevant. The remaining series are treated according to normal procedures.
• Raw and seasonally adjusted data are available.
• In addition to raw data, at least one of the following series is released: pre-treated, seasonally adjusted, seasonally plus working day adjusted, trend-cycle series.
• Both levels/indices and different forms of growth rates are presented.
• For each series, some quality measures of the seasonal adjustment are presented.
Statistics Norway’s metadata on methods: seasonal adjustment
The Committee for Monetary, Financial and Balance of Payments statistics: ESS-Guidelines on seasonal adjustment
EUROSTAT: Seasonal Adjustment. Methods and Practices
US census: X-12-ARIMA-manual
Dinh Quang Pham: Nye US Census-baserte metoder for ukedageffekter for norske data, Notater 2008/58, Statistisk sentralbyrå
Ole Klungsøyr: Sesongjustering av tidsserier. Spektralanalyse og filtrering, Notat 2001/54, Statistisk sentralbyrå
Dinh Quang Pham: Innføring i tidsserier - sesongjustering og X-12-ARIMA, Notater 2001/2, Statistisk sentralbyrå