1. JDemetra+: Latest features by the BBk (X-11 part) SAEG Meeting, Frankfurt am Main, 7 June 2016 Andreas Lorenz, Deutsche Bundesbank, Statistics Department

2. Outline 1.Release 2.1.0 2.Current status of the development of plug-ins 3.Customized output plug-in: Brief overview 4.Promotion of JDemetra+ 7 June 2016 Page 2 Andreas Lorenz, Deutsche Bundesbank

3. 1. Current status of the development of the X-11 part (for JD+ 2.1) 7 June 2016 Page 3 Andreas Lorenz, Deutsche Bundesbank TaskStatus X11 { } spec  “calendarsigma” argument Release 2.1. (GitHub)  X11 { } spec  “excludefcst” argument Release 2.1. (GitHub)  Assignment of user-defined regression effects to the component type Release 2.1. (GitHub)  Provision of the MSR (moving seasonality ratio) in the output Release 2.1. (GitHub) 

4. 2. Current status of the development of plug-ins: 7 June 2016 Page 4 Andreas Lorenz, Deutsche Bundesbank TaskStatus Aggregation of chain-linked seriesAlmost completed  https://github.com/bbkrd/KIX https://github.com/bbkrd/KIX SpecParser: translation of *.spc files from Census X-12 ARIMA to JDemetra+ Almost completed  https://github.com/bbkrd/SpecParser https://github.com/bbkrd/SpecParser TransReg: tool for transforming regression variables Draft of prototype finalised - BBk internal Customized output (controlled current adjustment report) Draft of prototype finalised (  section 3) - BBk internal

5. 3. Customized Output Plug-In: Brief overview  Summarizes all relevant information for performing seasonal adjustment with the controlled current adjustment approach, including  Sigma limits for extreme values and critical value for outliers  Seasonal and trend filters  F-Tests for seasonality  Moving Seasonality Ratios (MSR)  Table D 8.B of the Census method (SI ratios)  Flags for extreme values and outliers  Previously forecasted and newly estimated seasonal factors 7 June 2016 Page 5 Andreas Lorenz, Deutsche Bundesbank

6. 3. Customized Output Plug-In: Brief overview (cont’d)  Quick refresher: “Controlled current adjustment” Forecasted seasonal and calendar factors derived from a current adjustment are used to seasonally adjust the new or revised unadjusted data. However, an internal check is performed against the results of the “partial concurrent adjustment”, which is preferred if a significant difference exists. This means that each series needs to be seasonally adjusted twice. The approach is only practicable for a limited number of important series. A full review of all seasonal adjustment parameters should be undertaken at least once a year and whenever significant revisions occur (e.g. annual benchmark). ESS Guidelines on seasonal adjustment, 2015 Edition, Section 4.2 7 June 2016 Page 6 Andreas Lorenz, Deutsche Bundesbank

7. 3. Customized Output Plug-In: Brief overview (cont’d) - Example of a typical use case - 7 June 2016 Page 7 Andreas Lorenz, Deutsche Bundesbank

10. Upper window: Spec summary Lower window: Table D 8.B

16. Click on series name with right mouse button opens new context menu

20. 4. Promotion of JDemetra 2.1+ Hackathon (15.-16. March 2016)  Participation of the Bundesbank developers in the hackathon in three different sessions (Codathon, JDemetra+ and R, Quality reporting) Conference of European Statistics Stakeholders (CESS 2016)  Organization of session “JDemetra+: An innovative expandable tool for seasonal adjustment, time series analysis and beyond” Training activities  Increasingly based on JDemetra+ 7 June 2016 Page 20 Andreas Lorenz, Deutsche Bundesbank

21. Thank you for your attention! Any questions/remarks? 7 June 2016 Page 21 Andreas Lorenz, Deutsche Bundesbank