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The European Conference on Quality in Official Statistics Rome, 8-11 July 2008

Statistical Quality Control: Pilot Experiments of Finnish Financial Survey Data H. Hella, T. Härkönen, M. Salmela. The European Conference on Quality in Official Statistics Rome, 8-11 July 2008. The aims of this paper.

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The European Conference on Quality in Official Statistics Rome, 8-11 July 2008

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  1. Statistical Quality Control: PilotExperiments of FinnishFinancial Survey DataH. Hella, T. Härkönen, M. Salmela The European Conference on Quality in Official Statistics Rome, 8-11 July 2008

  2. The aims of this paper • Pilot statistical product control by combined cross-sectional and time series analysis for micro-economic data. • Select financial survey variables from a Finnish business survey. • Carry out very short-term rolling quality framework [t-1, t, t+1] for evaluating quality of respondent-wisedata. • Classify the survey respondents before analyses. • Choose the convenient modelling and smoothing tools for time series analyses (state-space model, Kalman technique, short-term robust smoothing). • Use in experiments the system form of structural time series models. European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  3. Statisticalmethodsused in pilotstudy • Descriptive statistical estimators • Descriptive and explorative graphics • Robust nonparametric smoother 4253H • Structural Time Series Model (STM) - vector model (system model) - cross-validation European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  4. Box-plot graph of the selected financial survey variables, t=2007:12 Source: EViews 6 Users Guide I, 2007 European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  5. QQ-plotcharts of stockseries of respondents 13, 16, 14 and 11 Variable 1 as an example: European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  6. STM estimation results for Variable 1 Stock value (ln), EUR m Period 2004:01 - 2007:09 Stock value (ln), EUR m Period 2004:01 - 2007:12 Market share, % Period 2004:01 - 2007:09 r16 = respondent 16 of survey Lvl = level, Slp = slope Q(p,q) is the Box-Ljung portmanteau statistic of residual autocorrelations. Q(p,q) is based on p first residual autocorrelations, q is the degrees of freedom of Khi Square distribution for testing. Statistical significance at the * 5%, ** 1 %, *** 0.1% level. European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  7. Rolling quality evaluation framework 1/2 • Operational time span: [t-1, t, t+1] • Optional thresholds to assess - model fitting value: time series analysis, estimate (t-1) and forecast (t) - nonparametric smoothed value: time series analysis, estimate (t-1) - market share value (cross-section) at time t-1 European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  8. Rolling quality evaluation framework 2/2 • Upper and lowerlimitsadded to thresholdvalue - rollingupper and lowerlimits of fittings and forecasts - scaleestimates of the residualsbased on smoothedhistoricalvaluesormovingform of std. deviation - std. deviationestimate of single marketsharehistoricaltimeseries • Possibleoutliersorstructuralbreaks inside qualityframework. European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  9. Rolling short-termqualityevaluation European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  10. Conclusions • Preliminaryinvestigation and classification of surveyrespondentswerehelpful for subsequentstatisticalanalyses. • ModernIT-techniques (multidim. Cube and ProClarity) createdbenchmark for combinedcross-sectional and timeseriesanalyses. • State-spacetimeseriesmodelling (Kalman filter) is convenient to analysesurveymicro-economic data. • Robustnon-parametricsmoothingsupportedmodelling to discoveroutliers and structuralbreaks. • Prerequisites for short-termrollingframework to evaluatestatisticalqualityareavailable for micro-economic data. European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  11. Main references: • Harvey, A. C. and Koopman, S. J. (1997), “Multivariate structural time series models”, in C. Heij, H. Schumaker, B. Hanzon and C. Praagman (eds.), Systematic Dynamics in Economic and Financial Models. Chichester: John Wiley. • Koopman, S. J., Harvey, A. C., Doornik, J. A. and Shephard, N. (2000), Structural Time Series Analyser, Modeller and Predictor, STAMP. Timberlake Consultants Press: London.  • Montgomery, D. C. (2001), Introduction to Statistical Quality Control, 4th Edition. John Wiley & Sons, Inc. • Rocke, D. M. (1989), Robust Control Charts. Technometrics, 31, 173-184. • Velleman, P. F. (1980), Definition and comparison of robust nonlinear data smoothing algorithms, Journal of the American Statistical Association, 75, 609-615. European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

  12. Thank you for your attention. Questions? heikki.hella@bof.fi tony.harkonen@bof.fi maija.salmela@bof.fi European Conference on Quality 2008, Rome, 8-11 July 2008 Hella H., Härkönen T., Salmela M.

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