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Using Influence Function Matrix As Outlier Detecting Tool Based On Pooled Serial Correlation Coefficients

Author

Listed:
  • S.R.T. MOENG
  • P.M. KGOSI
  • D.K. SHANGODOYIN

    (University of Botswana)

Abstract

In this paper, we incorporated autocorrelation function (ACF), partial autocorrelation function (PACF) and inverse auto-correlation function (IACF) into the influence function as a graphical tool for detecting outliers. Depending on the number of positive and negative values of the influence func-tion based on critical values obtained for different lags an observation is identify as outlier. Both the simulated data and Botswana meat sales data confirms the efficacy of using the pooled correlation co-efficients in influence function matrix as outlier detection device.

Suggested Citation

  • S.R.T. Moeng & P.M. Kgosi & D.K. Shangodoyin, 2009. "Using Influence Function Matrix As Outlier Detecting Tool Based On Pooled Serial Correlation Coefficients," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice (1954-2015), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 56, pages 576-585, November.
    • Handle: RePEc:aic:journl:y:2009:v:56:p:576-585
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