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Resampling m‐Dependent Random Variables with Applications to Forecasting

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  • Sara Sjostedt

Abstract

Resampling methods are proposed to estimate the distributions of sums of m‐dependent possibly differently distributed real‐valued random variables. The random variables are allowed to have varying mean values. A non parametric resampling method based on the moving blocks bootstrap is proposed for the case in which the mean values are smoothly varying or ‘asymptotically equal’. The idea is to resample blocks in pairs. It is also confirmed that a ‘circular’ block resampling scheme can be used in the case where the mean values are ‘asymptotically equal’. A central limit resampling theorem for each of the two cases is proved. The resampling methods have a potential application to time series analysis, to distinguish between two different forecasting models. This is illustrated with an example using Swedish export prices of coated paper products.

Suggested Citation

  • Sara Sjostedt, 2000. "Resampling m‐Dependent Random Variables with Applications to Forecasting," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(3), pages 543-561, September.
    • Handle: RePEc:bla:scjsta:v:27:y:2000:i:3:p:543-561
    DOI: 10.1111/1467-9469.00206
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    Cited by:

    1. Sjöstedt-de Luna, 2005. "Some properties of weakly approaching sequences of distributions," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 119-126, November.

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