On seasonal adjustment of Credit indicator C2

1. WHAT IS SEASONAL ADJUSTMENT?

2. PRE-TREATMENT

3. SEASONAL ADJUSTMENT

4. REVISION POLICIES

5. QUALITY OF SEASONAL ADJUSTMENT

6. SPECIFIC ISSUES ON SEASONAL ADJUSTMENT

7. DATA PRESENTATION ISSUES

8. REFERENCES

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 Credit indicator C2

On the basis of public holidays and holiday period in July and December the intensity of the supply and demand of credit fluctuates through the year. This complicates a direct comparison of gross debt figures from one month to the next. To adjust for these relations the gross debt is seasonally adjusted for the actual levels, so that one can analyse the underlying credit indicator development.

1.3 Seasonally adjusted series

The credit indicator statistics publishes five seasonally adjusted series; K1, K2, K2 foreign currency, K2-households and K2-non financial corporations. The seasonally adjusted figures for K2-foreign currency is not a result of own seasonal adjustment, but a residual from the difference of seasonally adjusted K2 and the seasonally adjusted K1.

2. PRE-TREATMENT

2.1 Pre-treatment routines/schemes

Pre-treatment is an adjustment for variations caused by calendar effects and outliers.

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.

2.2.1 Methods for trading/working day adjustment

2.2.2 Correction for moving holidays

2.2.3 National and EU/euro area calendars

2.3 Treatment of outliers

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

2.4 Model selection

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

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.

Comments: We have chosen compulsory multiplicative decompositions. The program chose automatically this option until 2008, and it is now incorporated as a claim.

3. SEASONAL ADJUSTMENT

3.1 Choice of seasonal adjustment approach

3.2 Consistency between raw and seasonally adjusted data

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

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.

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.

Comments: The total is computed independently of the components. The last component is computed as a residual of the difference between the total and the other components.

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.

Comments: The data is used for K1 and K2 from January 1989 to the last observed December figure. For K2-households and K2-non financial corporations the data is used from December 1987 to the last observed December figure.

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.

Comments: The program for seasonal adjustment with new seasonal components is made once a year, but seasonally adjusted figures are audited in accordance with audited raw data.

4.2 Concurrent versus current adjustment

4.3 Horizon for published revisions

Comments: The whole series which enters into seasonal adjustment is audited once a year. Apart from this, the elderly seasonal adjustment figures are only audited when unadjusted figures are been audited.

5. QUALITY OF SEASONAL ADJUSTMENT

5.1 Evaluation of seasonally adjustment data

5.2 Quality measures for seasonal adjustment

6. SPECIFIC ISSUES ON SEASONAL ADJUSTMENT

6.1 Seasonal adjustment of short time series

6.2 Treatment of problematic series

7. DATA PRESENTATION ISSUES

7.1 Data availability

7.2 Press releases

8. REFERENCES:

Statistics Norway's metadata - Statistical methods – Seasonal adjustment (http://www.ssb.no/english/metadata/methods/seasonal_adjustment.html)

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