Gross Domestic Product in Latvia, total (ESA 2010) — quarterly data

Gross Domestic Product (GDP) is one of the indicators in the System of National Accounts.

The System of National Accounts is a set of harmonised and conformable macroeconomic indicators, providing an overall picture of the economic situation and is widely used for economic analysis, forecasting and elaboration of the state policy.

Seasonally adjusted GDP data allow determining GDP as an economic indicator over a longer period of time.

The data are calculated in line with the following classifications:

• Statistical Classification of the Economic Activities in the European Communities (NACE Rev. 2);
• Classification of Institutional Sectors (ES);
• Classification of Individual Consumption According to Purpose (COICOP 2018);
• Classification of the functions of government (COFOG);
• Classification of Products by Activity (CPA version 2.1);
• Classification of Administrative Territories and Territorial Units of the Republic of Latvia (CATTU);
• Nomenclature of Territorial Units for Statistics (NUTS 2016);
• Classification of transactions and other flows.

Quarterly national accounts refer to the whole economy, but breakdowns by sectors are provided in the quarterly sector accounts: non-financial corporations, financial corporations, general government, households and non-profit institutions serving households.

National accounts aim to capture economic activity within the domestic territory. They combine data from a host of base statistics, and thus they have no common sampling reference frame. The elementary building blocks of ESA 2010 statistics are statistical units and their groupings. ESA 2010, defines two types of units, institutional units and local kind-of-activity units.

National accounts combine data from many source statistics. The concept of statistical population is not applicable in a national accounts context.

The whole economic territory of Latvia.

Since 1995

• GDP from production and expenditure approach is calculated also at constant prices: prices of the previous year and prices of the reference year. Calculation at prices of the previous year is based on the "annual average method" where the running quarter is calculated at the average prices of the previous year;
• To calculate GDP changes over a longer period it is “chain-linked” in one dynamic series with one reference year. Currently the year 2020 is used.

Euro at current prices, prices of previous year and constant prices of 2020, as a percentage of the total, changes compared to the previous year.

GDP from production and expenditure approach is calculated also at constant prices: prices of the previous year and prices of the reference year. Calculation at prices of the previous year is based on the "annual average method" where the running quarter is calculated at the average prices of the previous year.

To calculate GDP changes over a longer period it is “chain-linked” in one dynamic series with one reference year. Currently the year 2020 is used.

• Quarter

REGULATION (EU) No 549/2013 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 21 May 2013 on the European system of national and regional accounts in the European Union

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Confidentiality of the information provided is protected by Statistics Law: 

• Section 7, Paragraph two, Clause 8, which sets out the obligation of the statistical institution to ensure statistical confidentiality;
• Section 17, which defines the procedures for data processing and the requirements for data protection;
• Section 19, Paragraph one, which stipulates that official statistics must be disseminated in a way that does not allow either directly or indirectly identify a private individual or a State institution;
• Section 19, Paragraph two, which stipulates that the statistical institution shall publish the official statistics which have been produced within the framework of the Official Statistics Programme in a publicly available form and by a predetermined deadline on the portal of official statistics. Until the moment of publication of official statistics this statistics shall not be published

Confidential cells in business statistics are defined by using minimum frequency rule (n) and dominance criterion (n, k).

A cell in a table is considered safe if there are at least four contributors (respondents, n=4) and the share of the largest contributor in the total cell value does not exceed 80 % (1.80) or the share of two largest contributors in the total cell value does not exceed 90 % (2.90).

All official statistics are released according to the data release calendar at 13:00.

Advance dissemination calendar

Statistical release dates and times are pre-announced in the data dissemination calendar.

Quarterly

A quarterly press release on gross domestic product is published.

Not published.

Statistics database

Not available.

https://ec.europa.eu/eurostat/web/national-accounts/database

Eurostat decisions and guidelines are explained in the European System of Accounts 2010.

Quarterly National Accounts Inventory. Sources and methods of the Quarterly National Accounts for Latvia (PDF).

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To achieve high user satisfaction and ensure compliance with regulatory requirements, the CSB has introduced a Quality Management System (QMS). The system defines and, at the procedural level, describes processes of statistical production and identifies the persons responsible for their monitoring throughout all production stages. Its structure follows the principles of the Generic Statistical Business Process Model (GSBPM).

The QMS sets out the sequence in which processes are implemented – that is, the activities to be performed, including verifications of processes and produced statistics, the order and implementation requirements of these activities, and the persons responsible for their execution. It also defines the approach to evaluating production processes and their outcomes, and to implementing necessary improvements.

The CSB quality management system is certified to the ISO 9001:2015 standard Quality management systems — Requirements since 2018 (scope of certification: development, production and dissemination of official statistics). The original certification audit was performed by BM Trada Latvija SIA and a recertification audit, in 2024, was performed by Bureau Veritas Latvia SIA.

The CSB information security management system is certified to the ISO/IEC 27001:2022 standard Information security, cybersecurity and privacy protection — Information security management systems — Requirements since 2017 (scope of certification: collection, processing and storage of information and data for functions of the Central Statistical Bureau of Latvia. Provision of statistical information for inland and foreign users). The original certification audit was performed by BM Certification SIA and a recertification audit, in 2024, was performed by Bureau Veritas Latvia SIA.

The quality of statistics is assessed in accordance with the existing requirements of both external and internal regulatory enactments, as well as the established quality criteria.

Regulation (EC) No 223/2009 of the European Parliament and of the Council on European statistics stipulates that European statistics shall be developed, produced and disseminated on the basis of uniform standards and harmonised methods. In this context, the following quality criteria shall apply: relevance, accuracy, timeliness, punctuality, accessibility, clarity, comparability and coherence.

As the national statistical institute and the principal authority of the national statistical system, the CSB has set common general institutional-level quality requirements for authorities responsible for producing or providing national statistics. These requirements are based on the European Statistics Code of Practice, which comprises 16 principles.

The overall assessment of data quality is good.

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The task of the CSB is to produce reliable statistics to support the analysis of socio-economic processes and to inform future decision-making.

Send feedback on data quality to pasts@csp.gov.lv

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The geographical comparability of national accounts in Member States of the European Union is ensured by the application of common definitions of the European System of Accounts (ESA 2010).

The Statistical Office of the European Union (Eurostat) publishes on its website information on gross domestic product for the EU-27 and for each country separately - the National Accounts section of Eurostat.

Worldwide geographical comparison is also possible as most non-European countries apply the System of National Accounts (SNA 2008) guidelines, and SNA 2008 is consistent with ESA 2010.

The OECD publishes Member State data.

Comparable data for the period since 1995.

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Within the system of national accounts there is full consistency between the domains: annual and quarterly national accounts. They are usually the result of vintage differences.

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Within the system of national accounts there is full consistency between the domains: sector accounts, financial accounts, regional accounts, supply and use tables. However, in practice full consistency may not always be possible and temporary discrepancies might occur. They are usually the result of vintage differences.

One of the CSB priorities, in line with the strategic directions of the European Statistics System and current approaches to producing statistics, is to collect data through broader use of administrative sources together with regular CSB surveys, while proportionately reducing response burden.

In cooperation with administrative data holders and within the competences set out in the Statistics Law, CSB is addressing the issues related to the use of administrative data to ensure that the sources used are as complete and reliable as possible, helping to reduce the administrative burden on businesses and households.

CSB measures to improve the use of administrative data and reduce response burden (2024) – available in Latvian only.

Revision policy is an important component of good governance practice. The aim of the CSB Revision Policy is to define how the statistics produced and published by the CSB are reviewed and revised. The first chapter explains the main terms used in the document, the second chapter provides a brief description of the CSB Revision Policy, and the third chapter outlined the revision cycle for the statistics produced by the CSB.

Revision policy guidelines (8 September 2022, PDF, 597 KB)

In line with the Guidelines for CSB Revision Policy, the published data are adjusted after the balancing of quarterly or annual national accounts.

GDP revisions in 2025 (PDF)

GDP revisions in 2024 (PDF)

GDP revisions in 2023 (PDF)

GDP revisions in 2022 (PDF)

GDP revisions in 2021 (PDF)

GDP revisions in 2020 (PDF)

GDP revisions in 2019 (PDF)

- From the production approach:

Download (XLS)

- From the expenditure side:

Download (XLS)

Revision of GDP data

1st quarter of 2021 – 4th quarter of 2024 (PDF)

Calculations are made in line with the methodology of the European System of Accounts (ESA 2010). Main data sources used in calculations are:

• Surveys of enterprises and institutions;
• Labour Force Survey;
• Data from the Budget, the Treasury, the State Revenue Service, the Bank of Latvia and the Financial and Market Commission;
• Household Budget Survey.

The calculations include also estimates for non-response, non-registered units, under-reporting to fiscal administration, income in kind, products produced for own consumption as well as income from illegal activities (e.g., sale of drugs).

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The GDP statistics from production and expenditure approach is calculated at current prices (registration and calculations are made at the actual prices of the respective period) and constant prices. The indicators at constant prices are expressed at prices of the previous calendar year and prices of the reference year (chain-linked).

To calculate GDP at the prices of the previous calendar year the actual prices of the previous calendar year are used as a base and the “annual average” method (where each running quarter (or year) is calculated at the average prices of the previous year) is used. To make the calculations, various deflators are used. Both volume indices and price indices may be used as deflators. The following price indices are used: consumer price index, producer price index, construction cost index, services producer price index, price indices of agricultural products, export unit value index, import unit value index. The following volume indices are used: change in number of employees and change in natural indicators (e.g., in removals, passenger number, freights, etc.).

To calculate GDP at the prices of the reference (base) year (currently, prices of 2020) the indices calculated from the GDP indicators at the prices of the previous year are used to chain-link the calculated volume indices with 2020.

Gross domestic product from the production approach is calculated as a sum of value added plus taxes on products minus subsidies on products.

Value added is calculated by subtracting intermediate consumption from the value of output of goods and services. Output refers to the total products created during the reference year. Intermediate consumption consists of the value of goods and services used during the production. Breakdown of the data by years provides information on value added at current prices at 2-digit level of NACE Rev. 2 classification.

Taxes on products added to the total value added cover the taxes paid at the sale of product, e.g., value added tax, customs and excise duties.

Gross domestic product from the expenditure side at current and constant prices is calculated by summing final consumption expenditure, gross capital formation, exports of goods and services and minus imports of goods and services.

Gross domestic product from the income approach is calculated at current prices only. When calculating GDP from the income approach, the data on the primary income of the economic activity: compensation of employees (wages and salaries in cash and kind and social contributions of employers), taxes on production and imports, subsidies, gross operating surplus and gross mixed income (including consumption of fixed capital).

Gross national income is calculated by summing gross domestic product with property income, compensation of employees and subsidies received from other countries and by subtracting property income, compensation of employees and taxes on production and imports paid to other countries.

In 2019, basing on OECD recommendations for improving the quality of quarterly GDP time-series, disaggregation of annual data by quarters method was changed. Previously, simple mathematical disaggregation method “pro–rata” was used, which divided changes of annual data level proportionally by quarters, thus keeping quarterly increase rates within annual framework, but creating the so-called “step effect” among years. Basing on the data disaggregation quality criteria put forward, as well as on "European Statistical System (ESS) guidelines on temporal disaggregation, benchmarking and reconciliation — 2018 edition", elaborated by the EU Statistics Bureau Eurostat in cooperation with statistical institutions of the member states, the CSB took a decision to change benchmarking method of the annual data by implementing optimal disaggregation of the time-series. After testing several methods, in compliance with quality criteria put forward, “Denton – Cholete” method was chosen.

The direct benchmarking is applied for components at the most detailed level necessary to produce. All aggregates are benchmarked indirectly by the aggregation of the benchmarked components. Indirect benchmarking of aggregates allows to preserve the consistency (additivity) between components and all aggregates. The Denton-Cholette method has been implemented using the R package tempdisagg “Methods for Temporal Disaggregation and Interpolation of Time Series” developed by Christoph Sax, Peter Steiner, and Tommaso Di Fonzo (https://cran.r-project.org/package=tempdisagg).

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Seasonally and calendar adjusted GDP indices are available in quarterly breakdown. Users must take into account the fact that upon adding data of a new period also the previous timeseries are recalculated.

Calendar and seasonal adjustment

A time series is a sequence of observations collected at regular time intervals, for example, a monthly time series. It characterises indicator changes or development thereof.  Seasonality and calendar effects are present in a large number of economic time series.

Seasonality or seasonal fluctuations of time series mean those movements, which recur with similar intensity in the same season each year. For example, each year Christmas shopping time can be observed in time series reflecting retail sales statistics. Change of seasons, social habits and influence of institutional factors are among the main causes of seasonality.

The calendar effects cover influence of calendar on time series. It is impact left by differing number of working days (or Mondays, Tuesdays and other days of the week) in months on changes of indicator. For example, number of working days differing among the months may affect goods produced time series.

When the time series are influenced by seasonality or calendar effects, it may be difficult to get clear understanding on indicator changes over the time.  Seasonal adjustment is made to eliminate seasonal fluctuations and calendar effects in time series.

As a result seasonally adjusted time series, from which seasonality and calendar effects have been removed, are produced. It means that seasonally adjusted time series provide an estimate for what is “new” in the series, for example turning points in trends, business cycle or irregular component.  Moreover seasonal adjustment results in calendar adjusted time series, in which calendar effects or varying number of working days in months has been eliminated. Specifics of seasonally adjusted statistics allows improving data comparability over time:

• Seasonally adjusted time series do not contain seasonal fluctuations and calendar effects, thus it is possible to compare, for example, data on the current month with the previous month's data.
• Calendar adjusted time series are not influenced by calendar effects and are used to compare, for example, statistics on current month with the data on corresponding month of the previous year.

The seasonal adjustment is made taking into account seasonal adjustment guidelines developed by the European Statistical System.

Software: JDemetra+
Seasonal adjustment method: TRAMO/SEATS
Last model revision: For data on the 2nd quarter of 2017