JD+ 3.0: The New Tool for Time Series Methods
JD+ 3.0 is a powerful tool for time series analysis developed by NBB, offering advanced statistical and IT features for official statistics. With main sponsors like Eurostat and ECB, it provides tools for seasonal adjustment, Java libraries, and more. The upcoming release will focus on high frequency modeling, interaction with other systems, and a variety of statistical methods. Check out the special topics for a detailed overview of the features and modules available in the upcoming release.
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Presentation Transcript
NBB contribution: JD+ 3.0, the new tool for time series methods. Jean.palate@nbb.be ONS, time series meeting 2018 JD+ 3.0 1 / 6
Outline JDemetra+ 3.0 Short overview Statistical aspects IT aspects Current status / schedule Special topics JD+ 3.0 2 / 6
JD+ Overview Tool on time series methods for official statistics Core: seasonal adjustment (Tramo-Seats, X12) Java libraries, free and open source Available on Github Main sponsors: Eurostat + ECB SAEG, SACE, SAUG Main developers: NBB (>90%), Bundesbank JD+ 3.0 3 / 6
Main features of the future release Statistical aspects High frequency Modelling REGARIMA, Structural time series Multi-variate approach Nowcasting, benchmarking, temporal disaggregation IT aspects Refactoring ( maintenance) Java 9, 10 Interaction with other systems WEB services, R JD+ 3.0 4 / 6
Current status / schedule Basic modules (time series, math, stats, state space framework, regarima ) OK (90%) Automatic regarima modelling main (default) blocks available (50%) SA: canonical decomposition, X11, STS, STL OK (90%) Other statistical methods (benchmarking, temporal disaggregation, calendarization, nowcasting ): 60% First release at the end of 2019 Main modules available in R as quickly as possible for testing. JD+ 3.0 5 / 6
Special topics Seasonal adjustment of high-frequency series REGARIMA for daily / weekly series Canonical decomposition of fractional airline (SEATS) STL / X11 Advanced SA modules Seasonal specific structural time series New trend/seasonal filters (generic approach) Nowcasting Mixed frequency dynamic factor model applications: GDP, inflation, soft data JD+ 3.0 6 / 6
