Northern Ireland Quarterly Index of Services Impact of Seasonal Adjustment Review

Northern Ireland Quarterly Index of Services

Impact of Seasonal Adjustment Review

Theme: Economy

Geographical area: Northern Ireland

Date: June 2025

Introduction

This report summarises the impact of the periodic seasonal adjustment review on the quarterly Northern Ireland Index of Services (IOS) estimates.

The IOS provides a timely indicator of change in the volume of output of the private sector services industries in Northern Ireland. Output estimates are calculated from the IOS aspect of the Quarterly Business Survey (QBS). The IOS has a sample size of approximately 3,600 businesses, covering all private service sector businesses with 100 or more employees and a representative sample of smaller businesses. The sample frame for the IOS is the Inter Departmental Business Register (IDBR), a register of all businesses registered for VAT and/or PAYE. More information on the IOS methodology can be found on the NISRA website.

A seasonal adjustment review was carried out in April 2026, by the IOS team within NISRA in conjunction with the Office for National Statistics (ONS), with the updated models implemented in the Quarter 1 2026 publication. The aim of the review was to ensure that the seasonal adjustment model utilised in each time series was appropriate and working well. The current IOS seasonal adjustment models had been determined in a previous ONS review in May 2025.

Background

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 IOS 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.

Review Methodology

The IOS seasonal adjustment review was carried out for each of the six quarterly series shown in Figure 1. For the first four series, the Series Name shows which sections of the UK Standard Industrial Classification (SIC) are included in that series. The SIC classifies business establishments and other statistical units by the type of economic activity in which they are engaged. The Retail Sales Index (RSI) includes those businesses with an economic activity of ‘Retail trade; except of motor vehicles and motor cycles’ (SIC 47).

Figure 1 Series reviewed April 2026

Series Name	Description
GI	Wholesale and retail trade; repair of motor vehicles and motorcycles; accommodation and food service activities
HJ	Transport, storage, information & communication
KLMN	Business services and finance
PQRS	Other services
RSI	Retail Sales Index
IOS	Index of Services

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.
From the date of the last review, there have been revisions to all the data that were previously reviewed due to updates to the GVA estimates (from 2019 to 2023), changes in the deflators, and an index rebase to 2023. Four quarters of additional data (Q1 to Q4 2025) have also been added.

Each series was reviewed using a seasonal adjustment program called X-13ARIMA-SEATS. The span of data used in the review was from Quarter 1 2005 to Quarter 4 2025. Each review included:

Assessment of whether the series is seasonal. Analysis of changes in economic output 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 5- or 7-term averages for quarterly data.

Reviewing the 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 model and parameter 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 the 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 are 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.

Findings of the review

The recommended seasonal adjustment models are shown in Figure 2. The KLMN series is no longer adjusted for seasonality and the IOS and PQRS series have revised models. Following the review, three additional additive outliers and two level shifts have been added to the model for the GI series. As a result of the impact of the COVID-19 pandemic many of the series continue to have additive outliers and/or level shifts during 2020 and 2021, with a ramp regressor included for the HJ series between Q3 2021 and Q1 2022.

Figure 2 seasonal adjustment models, reviewed April 2026

Series	Current Transform	Current Model	Current TMA	Current SMA	Current Regressors	Revised Transform	Revised Model	Revised TMA	Revised SMA	Revised Regressors
GI	None	(0,1,3)(0,1,1)	5	(3x5)	AO2020.2, AO2020.4 & AO2021.1	None	(0,1,3)(0,1,1)	5	(3x5)	LS2008.3 LS2010.3 AO2020.1 AO2020.2 AO2020.4 AO2021.1 AO2021.3 AO2023.3
HJ	Log	(0,1,2)(0,1,1)	5	(3x5)	AO2020.2, AO2020.3 & RP2021.3-2022.1	No change	-	-	-	-
KLMN	Log	(0,1,1)(0,1,1)	5	(3x5)	AO2020.2	No longer	adjusted	-	-	-
PQRS	None	(0,1,1)(0,1,1)	5	(3x3)	AO2006.2, LS2020.1, AO2020.2 & AO2021.1	None	(0,1,1)(1,1,1)	5	(3x3)	AO2006.2, LS2020.1, AO2020.2 & AO2021.1
RSI	Log	(2,1,2)(0,1,1)	5	(3x5)	AO2020.3 & AO2021.1	No change	-	-	-	-
IOS	None	(0,1,2)(0,1,1)	5	(3x5)	LS2010.3, LS2020.1, AO2020.2, AO2020.3 & LS2021.2	None	(0,1,1)(0,1,1)	5	(3x5)	LS2010.3, LS2020.1, AO2020.2, AO2020.3 & LS2021.2

TMA = Trend Moving Average
SMA = Seasonal Moving Average

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, i.e. an extreme irregular point, it may have an identifiable cause such as a strike, bad weather or a pandemic.

A level shift (LS) 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.

A ramp (RP) 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 level shift 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.

Impact of the review

Figure 3 below shows the absolute difference between the current seasonal adjustment model applied to the data and the revised seasonal adjustment model applied to the data expressed as a proportion, such that:

Absolute Revision = |yT – yt|/yt where yT = value from the revised model and yt = value from the current model.

The data changes from the revised models are reflected predominantly in shifting the level of the series while the patterns are generally preserved. Figure 3 shows that the impact of the revisions is small in all the reviewed series. The graphical comparisons can be seen in the Annex.

Figure 3 Series Absolute Revisions, Reviewed April 2026

Series	Full Span Mean	Last 3 Years Mean	Final Year Mean	Latest Data Point
GI	0.0033	0.0026	0.0028	0.0001
HJ	0.0000	0.0000	0.0000	0.0000
KLMN	0.0083	0.0050	0.0068	0.0107
PQRS	0.0023	0.0005	0.0008	0.0012
RSI	0.0000	0.0000	0.0000	0.0000
IOS	0.0001	0.0002	0.0003	0.0003

Review implementation

The revised seasonal adjustment models will be introduced in the IOS Q1 2026 publication. The seasonal adjustment models and parameters will continue to be reviewed annually, with the starting point for subsequent reviews being these revised models.

Revisions to the seasonally adjusted estimates will be made in accordance with the IOS published policy on revisions, informed by the ESS Guidelines on Seasonal Adjustment.

Annex: Seasonal adjustment time series comparisons

Figures are indexed to 2023=100

Contact Details

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Email: info@nisra.gov.uk

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