On seasonal adjustment of quarterly statistics on new orders

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 quarterly statistics on new orders

The main of seasonal adjustment is to remove changes that are due to seasonal or calendar influences to produce a clearer picture of the underlying behaviour.

1.3 Seasonally adjusted series

The overall index and groups according to the structure of SIC 2007 are published in the new orders received 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 an automatic pre-treatment of the raw data based on standard options in the seasonal adjustment tools.

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.

Comments: A few series is not adjusted for the number of working days.

2.2.2 Correction for moving holidays

Automatic correction. If performed by X-12-ARIMA, automatic correction of raw data will be based on US holidays.

Comments: Some series is not adjusted for moving holdidays.

2.2.3 National and EU/euro area calendars

Use of default calendars. The default in X-12-ARIMA is the US calendar.

2.3 Treatment of outliers

Outliers, or extreme values, are abnormal values of the series.

No preliminary treatment of outliers.

2.4 Model selection

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.

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.

Comments: Automatic decomposition is used for some series.

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.

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.

Do not apply any constraint.

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.

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.

Only part of the time series is used to estimate the correction factors and the model.

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.

Seasonally adjusted data are revised in accordance with a well-defined and publicly available revision policy and 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.

Comments: The trend filter stays permanent

4.3 Horizon for published revisions

The period of revisions is defined according to the characteristic features of the series based on information from the seasonal adjustment tool.

5. QUALITY OF SEASONAL ADJUSTMENT

5.1 Evaluation of seasonally adjustment data

Evaluation of quality based only on graphical inspection and descriptive statistics.

5.2 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.

Table of quality measurement for this statistics

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

All problematic series are treated in a special way.

Comments: Some series is not always corrected for either Easter and working days.

7. DATA PRESENTATION ISSUES

7.1 Data availability

Raw and trend 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.

8. REFERENCES:

For quarterly statistics on new orders:

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 ukedagseffekter for norske data, Notater 2008/58, Statistisk sentralbyrå

Dinh Quang Pham: Ny metode for påskekorrigering for norske data, Notater 2007/43, 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