About 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
Due to fixed and movable holydays and holidays in July and December the figures of man-weeks worked vary across the year. Also the number of employed persons and unemployed persons vary across the year, especially for the young people. Among other matters this is because of summer substitute and young people searching for summer jobs.
Seasonally adjusted series
We seasonally adjust 2 age-divided series (more/less than 24 year) separately for employed persons and unemployed persons, and add the two series afterwards.
For man-weeks worked we seasonally adjust 3 gender- and age-divided series: persons 15 – 24 years, males 25 – 74 years and females 25 – 74 years.
Due to no sample uncertainty, Registered unemployed and registered unemployed + government measures are also seasonally adjusted as a supplement to the LFS. The Register-figures are seasonally adjusted separately for 4 gender- and age divided (more/less than 24 year) series.
Pre-treatment is an adjustment for variations caused by calendar effects and outliers.
Running a detailed pre-treatment. This means using models which are specially adapted for the pre-treatment of the raw data for a given series.
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.
We perform calendar adjustments on all series showing significant and plausible calendar effects within a statistically robust approach, with RegARIMA.
Methods for trading/working day adjustment
Comments : LFS is surveying persons attachment to the labour market in whole reference weeks. The reference weeks are not split to follow the exact months of the calendar. Therefore, all the monthly data files include as many Mondays as Tuesdays and so on. The monthly data files include either 4 or 5 reference weeks, and the inflation factors adjust fore that. Due to this the number of working days in a month is only affected by holidays and national days off.
Correction for moving holidays
Correction based on an estimation of the duration of the moving holidays effects, specifically adjusted to Norwegian circumstances.
The seasonally adjusted time series for man-weeks worked are pre-adjusted if Easter is in March.
Due to sensitivity to single holidays or common days off, the man-weeks worked series in addition are pre-adjusted for 2 nd Easter day, May 1st, May 17th, Whit Monday and Ascension Day. (We also take account of the effect when two of these days occur on the same date, for instance Ascension Day on May 17th in 2007 and 2012 and on May 1st. in 2008).
In addition we pre-adjust man-weeks worked series for the number of common days off that falls on week days in December, and for the number of normal days in the week between Christmas and New Year's Eve that falls in December/January in the LFS. All regression variables are deseasonalized by subtracting the long term monthly average. Also the regression variables are fine tuned by a week multiplier to take account of that some months represent 4 reference weeks and other 5 reference weeks.
National and EU/euro area calendars
Use of the Norwegian calendar
Comments : We also take account of the fact that LFS don’t divide reference weeks (Monday – Sunday) between months, like in the ordinary calendar. For instance the holiday May 1 st may in the LFS be in April. Correspondingly may New Year's Day be in December in the LFS.
Treatment of outliers
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.
Comments : Only additive outliers are detected automatically.
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 : The pickmdl procedure in Version 0.3 of X-12-ARIMA is used with the standard method (=first) in the yearly identification of ARIMA models. (Then these automatically identified models are hard coded in the specification files.)
In the list of ARIMA models that are checked in procedure pickmdl, 3 extra ARIMA models are included in order to possibly take better account of the data acquisition in the LFS where interviewees are interviewed every 3 months for 2 year. The extra ARIMA models are:
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.
Automatic decomposition scheme selection.
Choice of seasonal adjustment approach
X-12-ARIMA Version 0.3
Consistency between raw and seasonally adjusted data
Impose the equality over the year of seasonally adjusted data to original data (e.g. sum or average).
Consistency between aggregate/definition of seasonally adjusted data
In some series, consistency between seasonally adjusted totals and the original series is imposed. For some series there is also a special relationship between the different series, e.g. GDP which equals production minus intermediate consumption.
Impose the equality between aggregated series and the component series.
Comments : The labour force is not seasonally adjusted. The labour force, seasonally adjusted is defined as the sum of employed persons, seasonally adjusted and unemployed persons, seasonally adjusted.
The equality is imposed by indirect approach.
Direct versus indirect approach
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.
Indirect approach where the seasonal adjustment of components occurs using the same approach and software, and then totals are derived by aggregation of the seasonally adjusted components.
Horizon for estimating the model and the correction factors
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
Comments : Due to a major revision of the LFS in 2006, we now let the time series start in 2006.
General revision policy
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.
Revisions second months in the quarter .
The figures from the first months in the quarter are preliminary. They are re-estimated, at the same time as the data from the second months in the quarter are estimated, supplemented with the extra interviewees that we got after the preliminary deadline and with more up to date or better auxiliary register variables. For the re-estimation, also more up to date auxiliary information about the whole population from the Register of Employee and the Central Population Register are utilized.
Quarterly revisions . The figures from the first two months in the quarter are preliminary. They are re-estimated at the end of the quarter supplemented with the extra interviewees that we get after the preliminary deadlines and with better auxiliary register variables. For the re-estimation, also more up to date auxiliary information about the whole population from the Register of Employee are utilized.
Yearly revisions . After seasonal adjustment, the levels are corrected to make the annual averages of the seasonally adjusted figures equal to the corresponding unadjusted figures from the LFS. The annual averages of a year are not ready until we publish the 4 th quarter figures. At that point we use new factors of level correction. Until the 4 th quarter publication is available, preliminary level correction factors from last year have to be used. For the estimation of the 4 th quarter also more up to date auxiliary information about the whole population from the annual Tax Register are utilized.
Concurrent versus current adjustment
Partial concurrent adjustment
The model, filters and calendar regressors are re-identified once a year and the respective parameters and factors re-estimated every time a new or revised data becomes available.
Comments : Additive outliers are detected automatically and parameters re-estimated every time new data becomes available.
Horizon for published revisions
The entire time series is revised in the event of a re-estimation of the seasonal factors.
Evaluation of seasonally adjustment data
Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.
Quality measures for seasonal adjustment
For most of the series, a selected set of diagnostics and graphical facilities for bulk treatment of data is used.
For more information on the quality indicators in the table see: metadata on methods: seasonal adjustment
Seasonal adjustment of short time series
All series are sufficiently long to perform an optimal seasonal adjustment.
Comments : As from 2006 the LFS was revised, among other things the lower age limit covered by the survey was lowered from 16 to 15. In order to avoid breaks in the series, we start all the time series in 2006.
Treatment of problematic series
Problematic series are treated in a special way only when they are relevant. The remaining series are treated according to normal procedures.
Comments : Due to large random variation (sample uncertainty) and small seasonal variation, we do not seasonally adjust all the main variables of the LFS gender divided. This is the case for unemployment, employment and for Employed persons man-weeks worked for persons 15-24 years. Afterwards, gender divided figures are constructed from seasonally adjusted figures by utilizing monthly gender distributions calculated from trend-cycle figures from additional unofficial seasonal adjustments of the LFS.
Three months moving averages of seasonally adjusted data and trend are available.
All metadata information associated with an individual time series is available.
Historical data are available to enable revision analysis.
Comments : In order to reduce the uncertainty, the presented series are three months moving averages of the seasonally adjusted figures. For instance the figure from September is the average of the estimates from August - October.
See previous releases for historical data.
Three months averages of seasonally plus working day adjusted series and trend-cycle series are released.
For each series, some quality measures of the seasonal adjustment are presented.