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Benchmarking
Reusable components based on the Kalman filter.
Department of Statistics. R&D Unit.
Objectives
FeaturesNew implementation of the traditional methodsAnalysis tools (residuals, likelihood, revisions, ...)
Technology / DesignReusability (batch processing → graphicalanalysis)Extensibility (new benchmarking methods)Openess (rich interfaces, various facets)
Technological response (I)
In-housedevelopments
(C#, VB.NET, Java, ...)
Nbb Libraries.NETJava
Commercial software
(Excel, ...)
Other components
Standard technologiesJava → Portability.NET (COM) → Windows realm
Technological response (II): OO-Design
"Conceptual" programming→ direct link with theory
Complexity management→ detailed information
Extensibility, reusability→ integration of new features→ modification of existing solutions
Statistical framework
Disaggregated series modelled by a single [regression] equation:
with:
Special case: ARIMA models (⊃ traditional methods)
Estimation:(Diffuse) Kalman filter and smoother: Durbin/Koopman
Treatment of:• Fixed unknown / diffuse regression effects• Log transformation: approximated, iterative (see Proietti)
ttt Xy µβ ][ +=SSFt ~µ
Demo
Goals
Windows application (NbbBenchmarking.exe)
Variety of specifications• ARIMA model for the residuals (disaggregated series)• Aggregation mode• Regression effects• Log transformation
VersatilityPerformances
Technologies, languages
OO-Design
Algorithms (KF)
Detailed results• Residuals analysis• Likelihood• Revisions history• Aggregated ARIMA model
Excel add-insVBA module (~NbbBenchmarking)
User-defined functions
Java appletMC experiments (Chow-Lin, diffuse/fixedunkown regression effects)
NbbBenchmarking (I)
Standard stand-alone application.
Variousspecifications.Detailed results
NbbBenchmarking (II)
Rich graphicalinterface
Treatment of long seriesInterpolation of missing valuesForecasts / backcasts
NbbBenchmarking (III)
Detailed analysis of the residualsFast appreciation bymeans of colouredsignals
NbbBenchmarking (IV)
RevisionshistoryAggregatedmodels
and othergoodies...
Excel add-in
Full integration
Self-containedsolution
User-defined functions in Excel
Fast solution forsimple problems
Interactive tool
Java applet
Complete Java solution
see Proietti (2004, 6.1) for furtherexplanations.
Concluding remarks
Freely downlable fromhttp://www.nbb.be/app/dqrd/index.htm
New applications → some caution !Extensions (SSF)
Other SSF (Durbin-Quenneville, ...)Multivariate SSF (2006 ?).
External collaboration welcome
Feedback on the current end products.Use of the library for programming ?
• → Documentation, user's guide, ...
Other similar approaches ?• → Exchange of modules, code, ...
Multivariate methods• → Methodological support, tests, ...
Other related topics• Structural models• Seasonal adjustment, ...
References
Di Fonzo, T. (2003), "Temporal disaggregation of economic time series: towards a dynamic extension", working papers and studies, European Communities.Durbin J. and Koopman S.J. (2001), "Time Series Analysis by State Space Methods". Oxford University Press.Gomez V. and Maravall A. (1994), "Estimation, Prediction, andInterpolation for Nonstationary Series With the Kalman Filter", Journal of the American Statistical Association, vol 89, n° 426, 611-624.Harvey, A.C. (1989), "Forecasting, Structural Time Series Models andthe Kalman Filter", Cambridge University Press.Proietti T. (2004), "Temporal disaggregation by State Space Methods: Dynamic Regression Methods Revisited", working papers and studies, European Communities.Wei, W.W.S and Stram D.O. (1986), "Temporal aggregation in the ARIMA process", Journal of Time Series Analysis, vol 7, n°4, 279-292.
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