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A Comparison of Methods for Bootstrapping in the Local Level Model

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  • Franco, Glaura C
  • Souza, Reinaldo C

Abstract

Bootstrap in time series models is not straightforward to implement, as in this case the observations are not independent. One of the alternatives is to bootstrap the residuals in order to obtain the bootstrap series and thus use these series for inference purposes. This work deals with the problem of assessing the accuracy of hyperparameters in structural models. We study the simplest case, the local level model, where the hyperparameters are given by the variances of the disturbance terms. As their distribution is not known, we employ the bootstrap to approximate the true distribution, using parametric and non-parametric approaches. Bootstrap standard deviations are computed and their performances compared to the asymptotic and empirical standard errors, calculated using a Monte Carlo simulation. We also build confidence intervals to the hyperparameters, using four bootstrap methods and the results are compared by means of the length, shape and coverage probabilities of the intervals. Copyright © 2002 by John Wiley & Sons, Ltd.

Suggested Citation

  • Franco, Glaura C & Souza, Reinaldo C, 2002. "A Comparison of Methods for Bootstrapping in the Local Level Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(1), pages 27-38, January.
  • Handle: RePEc:jof:jforec:v:21:y:2002:i:1:p:27-38
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    Cited by:

    1. Thiago R. Santos & Glaura C. Franco & Dani Gamerman, 2010. "Comparison of Classical and Bayesian Approaches for Intervention Analysis," International Statistical Review, International Statistical Institute, vol. 78(2), pages 218-239, August.
    2. Silva, Ivair Ramos & Ernesto, Dulcidia & Oliveira, Fernando & Marques, Reinaldo & Oliveira, Anderson, 2021. "Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 40(2), April.
    3. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
    4. Silva, E.M. & Franco, G.C. & Reisen, V.A. & Cruz, F.R.B., 2006. "Local bootstrap approaches for fractional differential parameter estimation in ARFIMA models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1002-1011, November.

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