Removing Seasonal Noise from Economic Data: How JDemetra+ Improves Official Statistics

The Asian Development Bank’s guide explains how JDemetra+, developed by the National Bank of Belgium, the Deutsche Bundesbank, and Eurostat, is used to remove seasonal and calendar effects from economic data so that real trends and turning points become clearer. It shows, in simple terms, how careful data preparation, calendar handling, modeling, diagnostics, and clear communication help produce reliable and transparent official statistics.

Economic data often move in regular patterns that repeat every year. Retail sales rise during holidays, farming output follows planting and harvest cycles, and the number of working days changes from month to month. These patterns can hide what is really happening in the economy. A rise or fall in data may look dramatic, but it simply reflects the calendar rather than real economic change. Seasonal adjustment removes these predictable effects so that analysts and policymakers can focus on true trends, turning points, and shocks. For official statistics such as gross domestic product, industrial production, or employment, this clarity is essential for sound economic decisions.

JDemetra+ and the Institutions Behind It

The Practical Guide on Seasonal Adjustment Using JDemetra+, published by the Asian Development Bank in December 2025, explains how to carry out seasonal adjustment using JDemetra+, an open-source software developed jointly by the National Bank of Belgium, the Deutsche Bundesbank, and Eurostat. The software is officially recommended by the European Statistical System and the European System of Central Banks and follows international standards such as the IMF’s Quarterly National Accounts Manual and the European Statistical System’s seasonal adjustment guidelines. JDemetra+ combines two globally accepted methods, X-13ARIMA-SEATS and TRAMO/SEATS, into one transparent and reproducible platform used widely by national statistical offices.

Preparing Data the Right Way

The guide stresses that good seasonal adjustment starts with good data. Time series must be long enough, consistent, and properly structured before any adjustment is attempted. Analysts are encouraged to visually inspect data to identify missing values, unusual spikes, sudden breaks, or changes in seasonal behavior. Series that are too short can produce unstable results, especially when calendar effects are involved. JDemetra+ provides tools to import data from spreadsheets and databases, organize them in structured workspaces, and explore them through charts and basic diagnostics. This early checking stage helps avoid errors later in the process and ensures that the adjustment is based on reliable information.

Handling Calendars, Holidays, and Models

A major source of distortion in economic data comes from calendars. Differences in working days, leap years, and holidays can strongly affect monthly or quarterly figures. The guide explains how JDemetra+ allows users to build national calendars that reflect country-specific holidays, including fixed dates, Easter-related holidays, and holidays that fall on specific weekdays. For countries with religious or lunar holidays that move each year, such as Ramadan or Eid, users can create their own external calendar variables. Once calendar effects are defined, analysts choose a seasonal adjustment specification that determines how the software handles transformations, outliers, and statistical models. While JDemetra+ provides ready-made templates, users are encouraged to adapt them to reflect the economic reality behind each series.

Checking Results and Explaining Them to Users

Seasonal adjustment does not end when the software produces a seasonally adjusted series. The guide places strong emphasis on checking results through diagnostics. These include tests to confirm that no seasonal patterns remain, checks that results are stable over time, and tools that show how estimates change as new data are added. JDemetra+ also helps users refresh results regularly and export both data and metadata in transparent formats. Finally, the guide highlights the importance of communication. Because seasonally adjusted figures are widely used but often misunderstood, statistical offices should clearly explain their methods, revision policies, and limitations. Good communication turns seasonal adjustment into a trusted public service rather than a technical mystery.