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Bayesian Inference for Autoregressive Models with Skewed Exponential Power Errors

비대칭 지수멱 오차를 가지는 자기회귀모형에서의 베이지안 추론

  • Received : 2014.09.01
  • Accepted : 2014.10.27
  • Published : 2014.12.31

Abstract

An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.

Keywords

Autoregressive model;Bayesian p-value;skewed exponential power distribution;Gibbs sampler;robust

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