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Block bootstrap prediction intervals for parsimonious first‐order vector autoregression

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  • Jing Li

Abstract

This paper attempts to answer the question of whether the principle of parsimony can be applied to interval forecasting for multivariate series. Toward that end, this paper proposes the block bootstrap prediction intervals based on parsimonious first‐order vector autoregression. The new intervals generalize standard bootstrap prediction intervals by allowing for serially correlated prediction errors. The unexplained serial correlation is accounted for by the generalized multivariate block bootstrap, which resamples two‐dimensional arrays of residuals. Different methods of block bootstraps are compared. A Monte Carlo experiment shows that, in most cases, the new intervals from a parsimonious model outperform the standard bootstrap intervals from a complex model. The proposed block bootstrap prediction intervals are illustrated using financial data for interest rates and exchange rates.

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  • Jing Li, 2021. "Block bootstrap prediction intervals for parsimonious first‐order vector autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 512-527, April.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:3:p:512-527
    DOI: 10.1002/for.2728
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