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 survey maps out business leaders’ evaluations of the economic situation and the outlook for a fixed set of indicators, such as production, employment and new orders received. Even if the survey does not give precise measures of economic variables, this kind of business survey helps to monitor the economic trend in present time and in the short-term outlook.
The level of activity will vary because of public holidays and vacations. In some industries there is also a seasonal variation in demand as a consequence of the different seasons. An example is the demand and production of certain food products, which will depend on whether it is summer or winter. These effects will influence the reported data for a number of indicators in the business tendency survey. These circumstances make it difficult to compare the data from quarter to quarter. To adjust for these effects the business tendency survey is seasonally adjusted, and in this way we are able to analyse the underlying effects which says something about the economic cycle from quarter to quarter. There is mainly the trend series which is published and commented. The exception is the industrial confidence indicators where the seasonally adjusted series are published.
5 seasonally adjusted series and about 140 trend series are published in the business tendency survey. The following table show the 5 seasonally adjusted series which is published:
Industry |
Indicator |
Manufacturing, mining and quarrying |
Industrial confidence indicator |
Manufacturing |
Industrial confidence indicator |
Intermediate goods |
Industrial confidence indicator |
Capital goods |
Industrial confidence indicator |
Consumer goods |
Industrial confidence indicator |
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
• No pre-treatment.
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.
• No calendar adjustment of any kind is performed.
• No correction.
• No correction.
• Definition of series not requiring calendar adjustment.
Outliers, or extreme values, are abnormal values of the series.
• No preliminary treatment of outliers.
Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.
• Manual model selection after running statistical tests.
Comments: (0, 1, 1)(0, 1, 1) The airline-model is chosen manually for all series
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 some of the series a logarithmic transformation is carried out
• 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.
• 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.
• All problematic series are treated in a special way.
• Raw and seasonally adjusted data are available.
• All metadata information associated with an individual time series is available.
Comments: The industrial confidence indicator is published as seasonally adjusted series for the aggregates manufacturing, mining and quarrying, manufacturing and the main industrial groupings; intermediate goods, capital goods and consumer goods. For most of the remaining indicators raw data and trend series are published. For some series only the trend series are published.
• 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.
• For the industrial confidence indicator, some quality measures of the seasonal adjustment are presented.
Statistic Norway 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