Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Study of Estimation Method for Auto-Regressive Model with Non-Normal Error and Its Prediction Accuracy

비정규 오차를 고려한 자기회귀모형의 추정법 및 예측성능에 관한 연구

  • Lim, Bo Mi (School of Industrial Management Engineering, Korea University) ;
  • Park, Cheong-Sool (School of Industrial Management Engineering, Korea University) ;
  • Kim, Jun Seok (School of Industrial Management Engineering, Korea University) ;
  • Kim, Sung-Shick (School of Industrial Management Engineering, Korea University) ;
  • Baek, Jun-Geol (School of Industrial Management Engineering, Korea University)
  • 임보미 (고려대학교 산업경영공학과) ;
  • 박정술 (고려대학교 산업경영공학과) ;
  • 김준석 (고려대학교 산업경영공학과) ;
  • 김성식 (고려대학교 산업경영공학과) ;
  • 백준걸 (고려대학교 산업경영공학과)
  • Received : 2013.01.31
  • Accepted : 2013.03.06
  • Published : 2013.04.15
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Abstract

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

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References

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