Business tendency survey for manufacturing, mining and quarrying, Q1 2013

Why do we seasonally adjust the business tendency survey?

The business tendency survey is part of a system of short term statistics to monitor the economy. The primary goal of the survey is to provide current data on the development in the business cycle for manufacturing, mining and quarrying. The survey does not give precise measures of economic variables, but it still provides useful information on the current situation and the short-term outlook.

The level of activity within manufacturing, mining and quarrying will vary throughout the year because of public holidays etc. Some industries also experience fluctuations due to a change of seasons. An example is the demand for and production of certain food products which depend on whether it is summer or winter. This kind of effects will influence the reported data for a number of indicators in the business tendency survey and make it difficult to compare the results from quarter to quarter.

The business tendency survey is subjected to a process of seasonal adjustment in order to remove the effects of seasonal fluctuations. In this way we are able to analyse the underlying development in the business cycle. It is mainly the smoothed seasonally adjusted time series (trend) that are released and analysed.

Time series that are seasonally adjusted

The business tendency survey publishes 220 seasonally adjusted time series which covers a wide range of indicators on the development within manufacturing, mining and quarrying and EUROSTAT's end-use categories (Main Industrial Groupings, MIG).

 

 

 

Pre-treatment routines/schemes

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

No pre-treatment is performed.

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.

No calendar adjustment is performed.

Methods for trading/working day adjustment

No adjustment for trading/working day is performed.

Correction for moving holidays

No correction for moving holidays is performed.

National and EU/euro area calendars

Calendar adjustment is not required.

Treatment of outliers

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

No pre-treatment of extreme values. 

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 performed manually based on statistical tests.

Comments: (0,1,1) (0,1,1) or the "Airline model" is selected manually for all the time series.

Decomposition scheme

The composition 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.

Comment: Log additive method is in use for some of the time series.

Choice of seasonal adjustment approach

X-12-ARIMA

Consistency between raw and seasonally adjusted data

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

Do not apply any constraint.

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.

Direct versus indirect approach

Direct seasonal adjustment is performed if all 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.

The unadjusted data are aggregated, and direct seasonal adjustment is performed on aggregates and components using the same approach and software. Any discrepancies across the aggregation structure are not removed.

Horizon for estimating the model and the correction factors

When performing seasonal adjustment on time series, it is possible to choose the number of observations to be used when estimating the model and the correction factors. Correction factors are the factors used in the pre-treatment and seasonal adjustment of the time series.

The whole time series is used to estimate the model and the correction factors.

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.

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.

Horizon for published revisions

The revision period for the seasonally adjusted results is limited to 3-4 years prior to the revision period of the unadjusted data, while older data are frozen.

Comment: The revision period for the seasonally adjusted figures is 4 years when new data are added. The whole time series may be revised when implementing new or improved methods.

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.

A table containing selected quality indicators for the seasonal adjustment is available here.

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.

Treatment of problematic series

All problematic series are treated in a special way.

Data availability

Unadjusted data, seasonally adjusted data and smoothed seasonally adjusted data are available.

Comments: Only unadjusted data are released for lower aggregates within manufacturing.

Press releases

In addition to raw data, at least one of the following series is released: Calendar adjusted, seasonally adjusted, smoothed seasonally adjusted (trend).