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Estimation of autoregressive models with epsilon-skew-normal innovations

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  • Bondon, Pascal

Abstract

A non-Gaussian autoregressive model with epsilon-skew-normal innovations is introduced. Moments and maximum likelihood estimators of the parameters are proposed and their limit distributions are derived. Monte Carlo simulation results are analysed and the model is fitted to a real time series.

Suggested Citation

  • Bondon, Pascal, 2009. "Estimation of autoregressive models with epsilon-skew-normal innovations," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1761-1776, September.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:8:p:1761-1776
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    References listed on IDEAS

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    1. Wong, Wing-Keung & Bian, Guorui, 2005. "Estimating parameters in autoregressive models with asymmetric innovations," Statistics & Probability Letters, Elsevier, vol. 71(1), pages 61-70, January.
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    Cited by:

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    2. Clécio da Silva Ferreira & Gilberto A. Paula & Gustavo C. Lana, 2022. "Estimation and diagnostic for partially linear models with first-order autoregressive skew-normal errors," Computational Statistics, Springer, vol. 37(1), pages 445-468, March.
    3. M. Sharafi & A. R. Nematollahi, 2016. "AR(1) model with skew-normal innovations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 1011-1029, November.
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    5. Zheng, Tingguo & Xiao, Han & Chen, Rong, 2015. "Generalized ARMA models with martingale difference errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 492-506.
    6. T. Manouchehri & A. R. Nematollahi, 2019. "Periodic autoregressive models with closed skew-normal innovations," Computational Statistics, Springer, vol. 34(3), pages 1183-1213, September.
    7. Byun, Manhee & Choe, Changgwon & Cheon, Seunghyun & Lee, Aejin & Lim, Hankwon, 2022. "Statistical and stochastic feasibility studies of potential liquid organic hydrogen carriers in a membrane reactor for simultaneous hydrogen storage and production: Technical, economic, and environmen," Renewable Energy, Elsevier, vol. 195(C), pages 1393-1411.
    8. Demetrescu, Matei & Golosnoy, Vasyl & Titova, Anna, 2020. "Bias corrections for exponentially transformed forecasts: Are they worth the effort?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 761-780.

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