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On the Method of Identification of Atypical Observations in Time Series

Author

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  • Oesterreich Maciej

    (West Pomeranian University of Technology, Szczecin, Poland, Department of Applied Mathematics in Economics, Faculty of Economics)

Abstract

The paper presents a method of detecting atypical observations in time series with or without seasonal fluctuations. Unlike classical methods of identifying outliers and influential observations, its essence consists in examining the impact of individual observations both on the fitted values of the model and the forecasts. The exemplification of theoretical considerations is the empirical example of modelling and forecasting daily sales of liquid fuels at X gas station in the period 2012-2014. As a predictor, a classic time series model was used, in which 7-day and 12-month cycle seasonality was described using dummy variables. The data for the period from 01.01.2012 to 30.06.2014 were for the estimation period and the second half of 2014 which was the period of empirical verification of forecasts. The obtained results were compared with other classical methods used to identify influential observations and outliers, i.e. standardized residuals, Cook distances and DFFIT. The calculations were carried out in the R environment and the Statistica package.

Suggested Citation

  • Oesterreich Maciej, 2020. "On the Method of Identification of Atypical Observations in Time Series," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 24(2), pages 1-16, June.
    • Handle: RePEc:vrs:eaiada:v:24:y:2020:i:2:p:1-16:n:1
    DOI: 10.15611/eada.2020.2.01
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    References listed on IDEAS

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    1. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.
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    More about this item

    Keywords

    forecasts; identification; multiple regression; time series; outliers;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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