On seasonal adjustment of Index of production

6. SPECIFIC ISSUES ON SEASONAL ADJUSTMENT

1. WHAT IS SEASONAL ADJUSTMENT?

1.1 What is 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

1.2 Why do we seasonally adjust index of production

The output in manufacturing will normally vary from month to month because of public holidays and vacations in July and December and other things. The main aim of seasonal adjustment is to remove changes that are due to seasonal or calendar influences to better able to compare the output in manufacturing from month to month.

1.3 Seasonally adjusted series

The overall index, sections, divisions and groups according to the structure of SIC 2007 as well as indexes classified according to EUROSTAT’s end-use categories (Main Industrial groupings MIG’s) are seasonally adjusted in the index of production in Norway (see Table 1)

2. PRE-TREATMENT

2.1 Pre-treatment routines/schemes

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.

2.2 Calendar adjustment

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). The regression variables for the calendar adjustment are adapted to reflect the working days, public holidays and so forth specific to Norway.

2.2.1 Methods for trading/working day adjustment

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.

2.2.2 Correction for moving holidays

Correction based on an estimation of the duration of the moving holidays effects, specifically adjusted to Norwegian circumstances.

2.2.3 National and EU/euro area calendars

Use of the Norwegian calendar or the EU/euro area calendar as appropriate; the EU/euro area calendar is based on the mean number of working days in the different member states.

Comments: The Norwegian calendar is in use

2.3 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.

2.4 Model selection

Pre-treatment requires choosing an ARIMA model, as well as deciding whether the data should be log-transformed or not.

Model selection is primarily automatic, but in some cases models are selected manually

Comments: Log transformation of the unadjusted figures is carried out

2.5 Decomposition scheme

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.
For series with zero or negative values, adding a constant to make the series positive and select the appropriate decomposition scheme.

Comments: Log additiv method is in use

3. SEASONAL ADJUSTMENT

3.1 Choice of seasonal adjustment approach

X-12-ARIMA

3.2 Consistency between raw and seasonally adjusted data

In some series, consistency between raw and seasonally adjusted series is imposed.

Do not apply any constraint.

Comments: (see comments 3.3)

3.3 Consistency between aggregate/definition of seasonally adjusted data

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.

Impose the equality between aggregated series and the component series.

Comments: Only equality between the overall index and extraction and related services, manufacturing, mining and quarrying, and electricity, gas and steam is imposed.

3.4 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.

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.

Comments: The overall index is a formula of extraction and related services, manufacturing, mining and quarrying and electricity, gas and steam supply.

3.5 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

4. REVISION POLICIES

4.1 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.

Both raw and seasonally adjusted data are revised between two consecutive official releases of the release calendar.

4.2 Concurrent versus current adjustment

The model, filters, outliers and regression parameters are re-identified and re-estimated continuously as new or revised data become available.

4.3 Horizon for published revisions

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: Seasonally adjusted numbers are updated from 2004

5. QUALITY OF SEASONAL ADJUSTMENT

5.1 Evaluation of seasonally adjustment data

Continuous/periodical evaluation using standard measures proposed by different seasonal adjustment tools.

5.2 Quality measures for seasonal adjustment

Additional specific tests are computed to complement the set of available diagnostics within the seasonal adjustment tool.

Table of quality measurement for this statistics (manufacturing SNN 10-33 SIC 2007) is available here.

For more information on the quality indicator in the table see: metadata on methods: seasonal adjustment

6. SPECIFIC ISSUES ON SEASONAL ADJUSTMENT

6.1 Seasonal adjustment of short time series

All series are sufficiently long to perform an optimal seasonal adjustment.

6.2 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: Only problematic series with huge inconsistencies will be treated in a special way.

7. DATA PRESENTATION ISSUES

7.1 Data availability

Raw and seasonally adjusted data are available.

7.2 Press releases

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.

8. REFERENCES:

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: New US-Census based methods for calendar adjustments on Norwegian data, Report 2008/ 58, Statistics Norway

Dinh Quang Pham: New method of Easter-adjustments on Norwegian data, Report 2007/43, Statistics Norway.

Ole Klungsøyr: Seasonal adjustemts of time series. Spectral analysis and filter, Report 2001/54, Statistics Norway

Dinh Quang Pham: Time series and basic seasonal adjustments and X-12-ARIMA, Reports 2001/2, Statistics Norway

ESS-Guidelines X-12-ARIMA-manual Seasonal Adjustment. Methods and Practices