Post on 01-Jan-2016
description
JDemetra+ (1.2.1)Luxembourg, 16/4/2013
Data providers IT improvements Methodological improvements
What's new ?
SDMX◦ .STAT (OECD) compatible
Automatic change of frequency ◦ Excel, ODBC...
Optimization◦ Caching...
Plug-ins ◦ Access databases
File-based (Access not needed)◦ Random Arima◦ SAS
Data providers
Correction of bugs, improvements of many features◦ Workspaces (storage...)◦ Graphical components (charts, grids...)◦ Properties Window◦ ...
Calendars and user-defined variables (graphical interface)◦ Demetra+ (not yet in the cruncher)
IT improvements
X11◦ Diagnostics
Calendars◦ Documentation
Arima estimation◦ Stdev of parameters ( + scores)◦ Optimisation procedure
Methodological improvements
Problem:◦ The likelihood function of complex models (AR
and MA parameters) have often several local maxima.
◦ Tramo, X12 and JD+(1.1.0) can lead to different solutions
◦ No "best" solution (with acceptable performances)◦ The solution is more dependant on the starting
point than on the Levenberg-Marquardt variant.
Solution in 1.2.1◦ Several starting points
Optimisation procedure
Comparison between: JD+ / TS JD+ / X12 TS / X12
Model: Arima [+calendar effects]
JD+ better
TSbetter = JD+
betterX12 better = TS
betterX12 better =
(0,1,1)(0,1,1)+TD7 0% 0% 100% 0% 0% 100% 0% 0% 100%
(1,1,1)(1,1,1)+TD7 3% 0% 97% 2% 0% 98% 0% 2% 98%
(2,1,1)(0,1,1)+TD7 4% 1% 95% 7% 0% 93% 4% 1% 95%
(3,1,1)(0,1,1) 18% 2% 80% 11% 1% 88% 6% 11% 83%
(1,1,3)(0,1,1) 19% 1% 80% 14% 1% 85% 5% 11% 83%
[1] "Better" means significantly higher likelihood (and thus different estimates).
Regular polynomials Seasonal polynomial
Log-LikelihoodAuto-regressive Moving
averageMoving average
φ(1) φ(2) φ(3) θ(1) Θ(1)
X12 -0.449 0.124 -0.079 -0.715 -0.733 -706.27
Tramo 1.089 0.424 0.136 0.931 -0.746 -703.31
JD+ 0.761 0.326 -0.031 0.622 -0.779 -702.57
Estimation for a (3 1 1)(0 1 1) model
01/0
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TramoJD+
Tramo-Seats and JD+. SA series based on the same model (different parameters estimation)
Comparison is not so simple Impact of the estimation problem on the
whole AMI◦ Differencing: (1 x 1)(1 x 1)◦ Arma identification◦ Last resort model (3 1 1)(0 1 1)
Comparability depends on the set of series:◦ Simple models (airline...) -> Highly comparable
results◦ Complex models -> Many different results
Consequences
Tramo-Seats◦ Integration of the last modifications of the core
engine. ?
Next steps