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On sieve bootstrap prediction intervals

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  • Alonso, Andrés M.
  • Peña, Daniel
  • Romo, Juan

Abstract

In this paper we consider a sieve bootstrap method for constructing nonparametric prediction intervals for a general class of linear processes. We show that the sieve bootstrap provides consistent estimators of the conditional distribution of future values given the observed data.

Suggested Citation

  • Alonso, Andrés M. & Peña, Daniel & Romo, Juan, 2003. "On sieve bootstrap prediction intervals," Statistics & Probability Letters, Elsevier, vol. 65(1), pages 13-20, October.
    • Handle: RePEc:eee:stapro:v:65:y:2003:i:1:p:13-20
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    References listed on IDEAS

    as
    1. Alonso Fernández, Andrés Modesto & Peña, Daniel & Romo, Juan, 2000. "Forecasting time series with sieve bootstrap," DES - Working Papers. Statistics and Econometrics. WS 9858, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. E. J. Hannan & L. Kavalieris, 1986. "Regression, Autoregression Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(1), pages 27-49, January.
    3. Bühlmann, Peter, 1995. "Moving-average representation of autoregressive approximations," Stochastic Processes and their Applications, Elsevier, vol. 60(2), pages 331-342, December.
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    Cited by:

    1. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
    2. Juan Díaz Maureira & Gustavo Leyva Jiménez, 2009. "Proyección de la inflación chilena en tiempos difíciles," Monetaria, CEMLA, vol. 0(4), pages 491-522, octubre-d.
    3. Alonso, Andres M. & Sipols, Ana E., 2008. "A time series bootstrap procedure for interpolation intervals," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1792-1805, January.
    4. Rupasinghe, Maduka & Samaranayake, V.A., 2012. "Asymptotic properties of sieve bootstrap prediction intervals for FARIMA processes," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2108-2114.
    5. Ricardo Gimeno & José Manuel Marqués-Sevillano, 2009. "Incertidumbre y el precio del riesgo en un proceso de convergencia nominal," Monetaria, CEMLA, vol. 0(4), pages 451-489, octubre-d.
    6. Juan Carlos Pérez-Velasco Pavón, 2009. "Determinantes de la demanda por la denominación promedio de billete: el caso de México," Monetaria, CEMLA, vol. 0(4), pages 523-548, octubre-d.
    7. Liew, Venus Khim-Sen, 2008. "An overview on various ways of bootstrap methods," MPRA Paper 7163, University Library of Munich, Germany.
    8. Andrés Alonso & Ana Sipols & Silvia Quintas, 2013. "A single-index model procedure for interpolation intervals in time series," Computational Statistics, Springer, vol. 28(4), pages 1463-1484, August.
    9. Andrés Schneider, 2009. "Regímenes de flotación administrada: un enfoque de cartera," Monetaria, CEMLA, vol. 0(4), pages 549-584, octubre-d.
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    11. Bu Hyoung Lee, 2022. "Bootstrap Prediction Intervals of Temporal Disaggregation," Stats, MDPI, vol. 5(1), pages 1-13, February.

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