Northern Ireland Quarterly Index of Production
Introduction
This paper summarises the impact of the periodic Seasonal Adjustment
Review on the
Northern
Ireland Index Of Production (IOP) estimates. This review was carried
out in Quarter 1 2025. This paper provides background information on the
IOS, descriptions of the revisions that have been made to seasonal
adjustment and the impact of these revisions. It is normal practice for
economic estimates to be revised. IOP data are provisional and subject
to
revision
for a period of four quarters from the publication date.
The quarterly Index of Production (IOP) provides a timely indicator
of growth in the output of the private sector services industries in
Northern Ireland. This is defined as Standard Industrial Classification
(SIC) 2007 sections B, C, D and E. Output estimates are calculated from
the IOP aspect of the Quarterly Business Survey (QBS). The IOP has a
sample size of approximately 1,200 businesses, covering all relevant
companies employing 40 or more employees and those employing 0 to 39
employees and having a turnover of £10 million or more, along with a
representative sample of smaller businesses. The sample frame for IOP is
the Inter Departmental Business Register (IDBR), a register of all
businesses registered for VAT and/or PAYE.
More
information on the IOP methodology can be found on the NISRA
website.
A Seasonal Adjustment Review for IOP was carried out in May 2025,
with the time series primarily reviewed by Francis Dunnett of the
UK
Office for National Statistics (ONS). The aim of the review was to
ensure that the seasonal adjustment model utilised in each time series
was appropriate and working well. The existing IOP seasonal adjustment
model had been determined in a previous ONS review in July 2023.
Review Objectives
Economic output data can be affected by events throughout the year
given that some work may be seasonal (for example, retailers may
generate greater levels of turnover during the Christmas period or home
heating oil suppliers may generate more turnover in winter). Output
estimates from IOP are seasonally adjusted to account for such seasonal
trends. Over time these seasonal patterns can change which necessitates
periodic reviews of existing seasonal adjustment models.
The 20
quarterly series reviewed are shown in Figure 1. The series name is a
code that is used to refer to each series throughout this report,
consistent with the names used in the previous review for the same
series. The businesses which make up each series are identified by their
Standard Industrial Classification (SIC) code which classifies business
establishments and other statistical units by the type of economic
activity in which they are engaged.
Figure 1 Series Reviewed May 2025
| Series Name | Description |
|---|---|
| IOP | Overall Index of Production |
| ALL C | Manufacturing sector (SIC07 Section C) Sector |
| E | Water supply, sewerage, and waste management (Inc. recycling) (SIC07 Section E) Sector |
| ALL D | Electricity, gas, steam and air conditioning supply (SIC07 Section D) Sector |
| B | Mining and quarrying (SIC07 Section B) Sector |
| ENG | Engineering and allied industries, Manufacturing Subsector |
| TOTOTHER | Total Other manufacturing, Manufacturing Subsector |
| CA | Food products, beverages and tobacco, Manufacturing Subsector |
| CH | Basic and fabricated metal products, Manufacturing Subsector |
| CECF | Manufacturing of chemical and pharmaceutical products, Manufacturing Subsector |
| CB | Textiles, leather and related products, Manufacturing Subsector |
| Consumer | Consumer goods are the final goods produced by industry which are intended for purchase by private consumers. These goods are consumed by market rather than used in the production of another good and are therefore closely linked to consumer demand and the factors which influence this |
| Intermediate | Intermediate goods are those purchased by the industry as inputs into the final production of goods. This category would include materials (for example, cement, rubber, plastic, chemicals and electronics) which will ultimately be used to produce a good for consumption |
| Investment | Investment goods (capital goods) are goods which enable production. For example, plant, equipment and inventories used to produce goods for consumption. Investment increases if business wish to expand or upgrade existing equipment |
| CC | Wood & paper products & printing & reproduction of recorded media |
| CDCM | Other manufacturing |
| CG | Rubber plastic & non-metallic mineral products |
| CICJ | Computer, electronic, electrical & optical products |
| CK | Machinery and equipment n.e.c |
| CL | Transport equipment |
Any exact additive relations that hold between series before seasonal adjustment are not guaranteed to be preserved between the seasonally adjusted series. Such relations, however, will still hold approximately. The seasonal adjustment of each series was reviewed by ONS using X-13ARIMA-SEATS. Each review included:
- Assessment of whether the series is seasonal. Analysis is complicated by regular effects associated with the time of the year and the arrangement of the calendar that obscure movements. For example, retail sales rise each December due to Christmas and this may obscure underlying movements in the retail sales trend. The purpose of seasonal adjustment is to remove variation associated with the time of the year and the arrangement of the calendar. This helps users to interpret movement in the series between consecutive time periods.
- Choosing the appropriate decomposition type, that is, additive or multiplicative. In a multiplicative decomposition, the seasonal effects change proportionately with the trend. If the trend rises, the seasonal effects increase in magnitude, while if the trend moves downward the seasonal effects diminish. In an additive decomposition the seasonal effects remain broadly constant regardless of which direction the trend is moving in. In practice most economic time series exhibit a multiplicative relationship and hence the multiplicative decomposition often provides the best fit.
- Calculating prior adjustments to be made to the series before seasonal adjustment. For example: temporary prior adjustments for outliers and level shifts; and permanent prior adjustments for trading days, Easter effects and seasonal breaks.
- Selecting the ARIMA forecasting model. The purpose of ARIMA modelling is to identify systematic structural features in the history of the series. We assume that these features will continue to be present in the future and will use them to forecast future values. The ARIMA method provides a wide range of possible models, which have been found very effective in modelling typical socio-economic series showing trends, seasonality and business cycle effects.
- Deciding the lengths of the seasonal and trend moving averages. Seasonal moving averages are weighted arithmetic averages applied to each quarter over all the years in the series i.e. a particular seasonal moving average is applied to each column of data. They are used by the X-13ARIMA-SEATS program to estimate the seasonal component of the series. The trend moving averages are weighted arithmetic averages of data along consecutive quarters. In general 9-, 13- or 23-term averages are used for monthly data and a 5- or 7-term for quarterly data.
- Reviewing X-13ARIMA-SEATS diagnostics, both quantitative and visual. The quality of a statistical output should be determined by its performance against a range of attributes that together can be used to assess whether an output meets users’ quality criteria.
The first stage of a review is a “default” run where all the
parameters choices (decomposition, ARIMA model, outliers, seasonal and
trend moving averages) are made automatically by X13ARIMA-SEATS. The
outcome from the default run is then refined with the over-riding aim
being to fit the simplest appropriate adjustment. The end result is then
compared with the choices made in any previous review. A decision to
alter previous recommendations, or to introduce complications, must be
supported by evidence and reasonable argument. User-defined files for
prior adjustments (rmx and ppp files) from the previous review were
tested for significance and updated where necessary e.g. if
transformation type for the series has changed.
This robust approach is taken to avoid uninformative revisions caused by minor changes to seasonal adjustment settings, changes that could easily revert back in the next review. A detailed description of the existing and recommended SA series can be found in Annex A.
Impact of Revisions
From the date of the last review six quarters of additional data have
been added for analysis, with the data series spanning from Q1 2005 to
Q4 2024. There have been revisions to all data that were previously
reviewed due to updates in the GVA estimates used and an index rebase to
2022.
Bearing this in mind Figure 2 below shows the absolute
difference between the current SA model data and the revised SA model
data expressed as a proportion, such that:
Absolute Revision = |yT
– yt|/yt where yT = value from the current review and yt = value from
the previous review.
The data changes from this review are
reflected predominantly in shifting the level of the series while the
patterns are generally preserved. Figure 2 shows that the impact of the
revisions is small in all the reviewed series. The graphical comparisons
can be seen in Annex B.
Figure 2 Series Absolute Revisions, June 2025
| Series | Full Span Mean | Last 3 Years Mean | Final Year Mean | Latest Data Point |
|---|---|---|---|---|
| IOP | 0.002 | 0.002 | 0.001 | 0.001 |
| ALLC | 0.001 | 0.001 | 0.000 | 0.000 |
| E | 0.006 | 0.005 | 0.004 | 0.003 |
| ALLD | 0.002 | 0.005 | 0.003 | 0.002 |
| B | 0.004 | 0.012 | 0.015 | 0.003 |
| ENG | 0.007 | 0.003 | 0.002 | 0.001 |
| TOTOTHER | 0.002 | 0.001 | 0.001 | 0.001 |
| CA | 0.001 | 0.002 | 0.002 | 0.002 |
| CH | 0.009 | 0.009 | 0.010 | 0.015 |
| CECF* | 0.000 | 0.000 | 0.000 | 0.000 |
| CB* | 0.000 | 0.000 | 0.000 | 0.000 |
| Consumer | 0.000 | 0.000 | 0.000 | 0.000 |
| Intermediate | 0.005 | 0.004 | 0.004 | 0.008 |
| Investment | 0.003 | 0.001 | 0.002 | 0.002 |
| CC* | 0.000 | 0.000 | 0.000 | 0.000 |
| CDCM | 0.005 | 0.009 | 0.010 | 0.021 |
| CG | 0.006 | 0.006 | 0.009 | 0.009 |
| CICJ | 0.003 | 0.004 | 0.003 | 0.007 |
| CK* | 0.000 | 0.000 | 0.000 | 0.000 |
| CL* | 0.000 | 0.000 | 0.000 | 0.000 |
*No change made to existing model
Analysis
The recommended seasonal adjustment is shown in Annex A. Eight of the
series have a new model (IOP, E, B, Eng, TOTOTHER, Intermediate,
Investment and CICJ) which is reflected, in part, in the higher absolute
revision value for those series in Figure 2. As a result of the impact
of Coronavirus, many of the series have seen additive outliers applied
for the 2020 to 2022 period. An additive outlier (AO) is a data point
which falls out of the general pattern of the trend and seasonal
component. Although an outlier may be caused by a random effect, that is
an extreme irregular point, it may have an identifiable cause such as a
strike, bad weather or a pandemic. Further level shifts (LS) during the
2020 to 2022 period for two of the series have been added, although the
majority of level shifts apply to periods identified in the previous
review in 2023. A level shift is an abrupt but sustained change in the
underlying level of the time series. The annual seasonal pattern is not
changed by a level shift. Similarly ramp regressors (Rp) were identified
in 9 of the series, a decrease from the 13 found in the 2023 review. A
ramp is a type of outlier used when a trend is changing too quickly to
be considered a natural movement of the trend itself yet is not an
instant change where a LS would be a better option. The decision to use
them is usually based on whether a sharp change in trend level causes
problems for the quality of the seasonal adjustment which proved to be
the case.
The revised SA models will be introduced in the IOP Q2
2025 publication results. Seasonal adjustment models and parameters will
be reviewed regularly, with the starting point for subsequent reviews
being the revised SA models outlined in Annex A.
Revisions to the
seasonally adjusted estimates will be made in accordance with the
IOP
published policy on revisions, informed by the
ESS
Guidelines on Seasonal Adjustment.
Annex A Seasonal Adjustment Models
| Series | Current Transform | Current Model | Current TMA | Current SMA | Current Regressors | Revised Transform | Revised Model | Revised TMA | Revised SMA | Revised Regressors |
|---|---|---|---|---|---|---|---|---|---|---|
| IOP | Log | (2 1 0)(0 1 1) | 5 | (3x9) | Rp2008.2-2009.2, LS2012.2, Rp2017.1-2017.3, LS2020.1 & AO2020.2 | Log | (0 1 0)(0 1 1) | 5 | (3x5) | Rp2008.3-2009.2, LS2017.2, AO2017.3 & AO2020.2 |
| ALLC | Log | (0 1 1)(0 1 1) | 5 | (3x9) | Easter[1], Rp2008.2-2008.4, Rp2017.1-2017.3 & AO2020.2 | Log | (0 1 1)(0 1 1) | 5 | (3x9) | Easter[8], QI2008.2-2008.4, LS2017.2, AO2017.3 & AO2020.2 |
| E | Log | (0 1 1)(0 1 1) | 5 | (3x5) | AO2020.2, AO2020.3 | Log | (0 1 3)(0 1 1) | 5 | (3x5) | AO2012.1, AO2013.1, AO2020.1, AO2020.2, AO2020.3 & AO2021.1 |
| ALLD | Log | (1 0 1)(0 1 1) | 5 | (3x3) | AO2005.2, LS2008.1 & TC2020.2 | Log | (1 0 1)(0 1 1) | 5 | (3x3) | AO2005.2, LS2008.1, TC2020.2, AO2021.4 & AO2022.1 |
| B | None | (2 0 0)(0 1 1) | 5 | (3x5) | LS2020.3 & LS2021.1 | None | (1 0 2)(0 1 1) | 5 | (3x5) | LS2021.1 & LS2023.3 |
| ENG | Log | (0 1 1)(0 1 1) | 5 | (3x9) | Rp2008.3-2009.2 & AO2020.2 | Log | (0 1 2)(0 1 1) | 5 | (3x3) | AO2020.2 |
| TOTOTHER | Log | (0 1 0)(0 1 1) | 5 | (3x5) | Rp2008.2-2009.2, AO2013.3, LS2018.4, AO2020.2 & LS2021.1 | Log | (0 1 2)(1 1 2) | 5 | (3x5) | Easter[8], td1coef, Rp2008.2-2009.1, AO2013.3, LS2018.4, AO2020.2 & LS2021.1 |
| CA | Log | (0 1 1)(0 1 1) | 7 | (3x9) | AO2005.3, AO2005.4 & Rp2017.1-2017.3 | Log | (0 1 1)(0 1 1) | 7 | (3x9) | AO2005.3, AO2005.4, Rp2017.1-2017.3 & AO2020.2 |
| CH | Log | (0 1 1)(0 1 1) | 5 | 3x9 | Easter[1], Rp2008.2-2008.4 & AO2020.2 | Log | (0 1 1)(0 1 1) | 5 | 3x9 | Rp2008.2-2008.4 & AO2020.2 |
| CECF | No Adjustment needed | - | - | - | - | - | - | - | - | - |
| CB | Log | (0 1 1)(0 1 1) | 7 | (3x9) | LS2019.1 & AO2020.3 | No revisions made | - | - | - | - |
| Consumer | Log | (0 1 1)(0 1 1) | 5 | (3x3) | AO2005.3, AO2005.4, Rp2017.1-2017.3 & AO2020.2 | Log | (0 1 1)(0 1 1) | 5 | (3x5) | AO2005.3, AO2005.4, Rp2017.1-2017.3 & AO2020.2 |
| Intermediate | Log | (0 1 1)(2 1 2) | 5 | (3x5) | Rp2008.4-2009.2 & AO2020.2 | Log | (0 1 2)(0 1 1) | 5 | (3x5) | Easter[15], Rp2008.3-2009.2, AO2020.2 & LS2022.2 |
| Investment | None | (3 1 1)(0 1 1) | 5 | (3x5) | Rp2008.4-2009.2 & AO2020.2 | None | (1 1 0)(0 1 1) | 5 | (3x5) | AO2020.2 |
| CC | Log | (0 1 1)(0 1 1) | 5 | (3x5) | Rp2008.2-2009.1, Rp2020.4-2021.2 & AO2020.2 | No revisions made | - | - | - | - |
| CDCM | Log | (2 1 2)(0 1 1) | 5 | (3x9) | AO2020.2 & AO2023.1 | Log | (2 1 2)(0 1 1) | 5 | (3x9) | LS2016.3 & AO2020.2 |
| CG | Log | (1 0 0)(0 1 1) | 5 | (3x5) | Rp2008.2-2009.1, Rp2018.3-2019.1, AO2020.2 & LS2020.3 | Log | (1 0 0)(0 1 1) | 5 | (3x5) | Rp2008.2-2009.1 & AO2020.2 |
| CICJ | Log | (0 1 1)(0 1 1) | 5 | (3x9) | LS2009.1, LS2009.2, LS2010.1, AO2011.4, LS2012.4, LS2015.4, AO2020.2 & AO2020.4 | Log | (0 1 2)(0 1 2) | 5 | (3x9) | LS2009.1, LS2009.2, LS2012.4, LS2015.4, AO2020.2 & AO2020.4 |
| CK | None | (0 1 1)(0 1 1) | 5 | (3x5) | Rp2008.2-2009.1 & AO2020.2 | No revisions made | - | - | - | - |
| CL | None | (0 1 1)(0 1 1) | 5 | (3x9) | LS2020.2 & AO2020.3 | No revisions made | - | - | - | - |
TMA = Trend Moving Average
SMA = Seasonal Moving Average
The “Current”
columns refer to the original method being used before the review and
“Revised” refers to the new method now being used.
Annex B Seasonal Adjustment Time Series Comparison
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