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An overview of bootstrap methods for estimating and predicting in time series

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  • Ricardo Cao

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  • Ricardo Cao, 1999. "An overview of bootstrap methods for estimating and predicting in time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 95-116, June.
  • Handle: RePEc:spr:testjl:v:8:y:1999:i:1:p:95-116
    DOI: 10.1007/BF02595864
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    References listed on IDEAS

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    1. Heimann, Günter & Kreiss, Jens-Peter, 1996. "Bootstrapping general first order autoregression," Statistics & Probability Letters, Elsevier, vol. 30(1), pages 87-98, September.
    2. Paparoditis, Efstathios, 1996. "Bootstrapping Autoregressive and Moving Average Parameter Estimates of Infinite Order Vector Autoregressive Processes," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 277-296, May.
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    Cited by:

    1. Barbeito, Inés & Cao, Ricardo, 2016. "Smoothed stationary bootstrap bandwidth selection for density estimation with dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 130-147.
    2. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    3. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    4. Castillo-Páez, Sergio & Fernández-Casal, Rubén & García-Soidán, Pilar, 2019. "A nonparametric bootstrap method for spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 1-15.
    5. Ruiz-Medina, M.D. & Romano, E. & Fernández-Pascual, R., 2016. "Plug-in prediction intervals for a special class of standard ARH(1) processes," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 138-150.
    6. Min-Hua Jen & Alex Bottle & Graham Kirkwood & Ron Johnston & Paul Aylin, 2011. "The performance of automated case-mix adjustment regression model building methods in a health outcome prediction setting," Health Care Management Science, Springer, vol. 14(3), pages 267-278, September.

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