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.