Resampling m‐Dependent Random Variables with Applications to Forecasting

Sara Sjostedt

Scandinavian Journal of Statistics, 2000, vol. 27, issue 3, 543-561

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.

Date: 2000
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