SASG, 16/10/2012

  What's new?

◦ ◦ ◦ ◦ ◦ Graphical interface Statistical issues I/O features Technology Tests Final remarks

Same main principles as .NET version • • • Interactive processing Rich results ...

Many improvements and goodies See the presentation

 ◦ ◦ Core engines (Java implementation) X12/X13:   Small improvements in comparison with the previous Java version Tramo-Seats: ≈ future release of Tramo-Seats (FORTRAN):    New AMI procedure New strategy in the case of non decomposable models Automatic choice of the "best" TD/WD effects (in progress)  ! Stochastic TD not integrated !

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Other SA methods

◦ Structural models, Generalized airline, Airline + seasonal noise

Benchmarking

◦ ◦ Univariate Cholette's method (see X13) Extension to the multi-variate case with contemporaneous and/or temporal constraints

Seasonality tests

◦ See Tramo

Direct/indirect comparison

◦ Use of multi-variate benchmarking if need be.

Calendars

◦ Weights on holidays

  ◦ Improved access to external data ODBC, Spreadsheet (Excel or OpenOffice)...

◦ ◦ ◦ ◦ More output (larger set of results) Csv Csv matrix Excel Txt

External packages JTsToolkit Core algorithms Peripheral modules NetBeans JDemetra+ plug ins In house developments Pure Java NetBeans modules Third party plug-ins

   JDemetra+ = NetBeans application What is NetBeans ?  IDE for Java developments (and others)  Framework for extensible applications  Sponsored by Oracle Benefits of using NetBeans?

◦ Extensible architecture   Allows independent development teams New features = new plug-ins (no impact on the existing modules)

◦ ◦ ◦ Numerous functionalities  Rich graphical interface  Management of the plug-ins  Automatic updates...

(Well) documented framework Large developers' community ◦       So, JDemetra+ = { plug-ins} Core SA (TramoSeats, X13...) Advanced SA Benchmarking ...

Data providers ...

 ◦ ◦ ◦ Profound re-engineering of Demetra+ (.NET) Better design of the algorithmic modules     Extensible algorithms  Faster processing (huge use of multi-processing) Improved I/O of the high-level components Generic xml serialization Facilities for providing results to other environments Designed for  (Quasi-)immediate WEB services  Development of macro-languages (?) New design of the other modules to fit higher (NetBeans) modularity

 ◦ ◦ Direct calls to the algorithmic routines Should be the preferred solution in many cases.

From simple high level modules...

◦ ...to all details ◦ ◦ ◦ Depends only on 1 library (jtstoolkit.jar) Documentation based on examples (in progress) Training in November

 ◦ ◦ ◦ Contents of the JTsToolkit API (≈50% code) Basic mathematical tools   Matrices, polynomials, filters, optimization, ...

Advanced time series model Statistical tools Descriptive statistics, statistical distributions, ..., state-space framework  ◦ Designed to develop rapidly new features For examples:     New tests (Canova-Hansen...) Time-varying TD. Temporal disaggregation (largely developed) ...

  Must be NetBeans modules (-> for advanced users/developers) ◦ ◦ ◦ ◦ Possible extensions (open list) Modification of the existing features (graphical components, menus...) New data providers, new diagnostics, new output New seasonal adjustment methods (automatic integration in batch processing...) New statistical methods (integration in the main menus, in the workspace...) Such extensions will be installed as plug-ins, without modification of the existing modules.

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Needs for test

◦ validation of the statistical methods

Type of algorithms

◦ "Fuzzy" problems: ◦  Example: seasonal adjustment methods...

 Difficult to develop a true strategy.

" Implicit" problems: ◦  Example: non linear optimization (ML estimation)...  Possibility to compare the results. No way to guarantee an optimal algorithm "Analytical" problems:   Example: Estimation of a Reg-Arima model...

Possibility to validate an algorithm

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Main tests developed in JDemetra (type 3)

◦ Likelihood computation of Reg-Arima models ◦  Comparison of Kalman filter (Tramo), Ljung-Box algorithm( X13) and others (Ansley...). Solution of linear models ◦ ◦    Comparison of QR, LU, singular value decomposition...

Canonical decomposition  Verification through decomposition/aggregation Many small other tests Benchmarking, statistical distributions, auto-correlations, linear filters, Easter...

Verification by:   comparison of alternative algorithms logical tests (checking of some constraints...)  tests by reciprocal transformation ◦ Further tests needed

   Huge developments supported by NBB and Eurostat NBB will ensure a minimal service of maintenance: ◦ ◦ ◦ Correction of bugs Training Add-ins following the needs and the agenda of NBB Necessity to organize the future