# 13bh -- Volume index of industrial output (2015=100), 1995M01-2023M12

## Choose variables

Yes

2/9/2024

- Share of industry in total industries (BCD):
- %
- Original index:
- Index point
- Annual change of the original index series, %:
- %
- Cumulative annual change of the original index series, %:
- %
- Working day adjusted index series:
- muutosprosentti
- Annual change of the working day adjusted index series, %:
- %
- Seasonally adjusted index series:
- muutosprosentti
- Change of the seasonally adjusted index series from the previous month, %:
- %
- Trend series:
- Index point
- Change of trend from previous month %:
- %

3/8/2024

2/9/2024

Yes

Statistics Finland, volume index of industrial output

001_13bh_2023m12

Field for searching for a specific value in the list box. This is examples of values you can search for.Share of industry in total industries (BCD) , Original index , Annual change of the original index series, % ,

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Field for searching for a specific value in the list box. This is examples of values you can search for.1995M01 , 1995M02 , 1995M03 ,

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Field for searching for a specific value in the list box. This is examples of values you can search for.BCD Total industries , B Mining and quarrying , C Manufacturing ,

Selected 0 of total 38

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Documentation of statistics
The volume index of industrial output describes the relative change in the volume of industrial output at fixed prices when compared with a specific base period. Original index, index adjusted for working days, seasonally adjusted index and trend.

1. Trend cycle (trend in brief) describes the long-term development and the movements caused by economic cycles in a time series.

2. Seasonal variation (caused by e.g. changes of season) describes annually recurring, almost regular changes.

3. As its name implies, irregular random variation occurs totally randomly. It cannot be included in the aforementioned components.

Seasonal adjustment means the estimation of seasonal variation and the elimination of its impact from a time series. The obtained outcome from this is a seasonally adjusted time series. The trend of a time series is obtained if both seasonal variation and irregular random variation are eliminated from it.

Phenomena associated with long-term development and cyclical changes are more easily observable from a seasonally adjusted time series and a trend of a time series. For instance, the detection of turning points in economic cycles becomes easier. The figures of a seasonally adjusted series are comparable with each other, which makes the comparing of two successive observations meaningful. The same also applies to the values of the trend of a time series.

Sometimes the number of working days in an observation period influences the value a time series receives. The Tramo/Seats method makes it possible to calculate a time series adjusted for working days in which the observations are comparable with regard to the weekly structure. This refers to making allowances for the impact of weekends, public holidays (e.g. Independence Day, Epiphany, May Day, Easter) and the Leap Day.

### Standard Industrial Classification (TOL 2008)

Yrityksen tai toimipaikan yritysrekisterin mukainen toimiala### Information

#### Annual change of the original index series, %

Alkuperäisen indeksipisteluvun muutos vuoden takaisesta prosentteina#### Trend series

It has generally been though that time series on economic trends are made up of different elements, or components:1. Trend cycle (trend in brief) describes the long-term development and the movements caused by economic cycles in a time series.

2. Seasonal variation (caused by e.g. changes of season) describes annually recurring, almost regular changes.

3. As its name implies, irregular random variation occurs totally randomly. It cannot be included in the aforementioned components.

Seasonal adjustment means the estimation of seasonal variation and the elimination of its impact from a time series. The obtained outcome from this is a seasonally adjusted time series. The trend of a time series is obtained if both seasonal variation and irregular random variation are eliminated from it.

Phenomena associated with long-term development and cyclical changes are more easily observable from a seasonally adjusted time series and a trend of a time series. For instance, the detection of turning points in economic cycles becomes easier. The figures of a seasonally adjusted series are comparable with each other, which makes the comparing of two successive observations meaningful. The same also applies to the values of the trend of a time series.

Sometimes the number of working days in an observation period influences the value a time series receives. The Tramo/Seats method makes it possible to calculate a time series adjusted for working days in which the observations are comparable with regard to the weekly structure. This refers to making allowances for the impact of weekends, public holidays (e.g. Independence Day, Epiphany, May Day, Easter) and the Leap Day.