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http://dx.doi.org/10.7737/JKORMS.2013.38.2.065

Applying Bootstrap to Time Series Data Having Trend  

Park, Jinsoo (용인대학교 경영정보학과)
Kim, Yun Bae (성균관대학교 시스템경영공학과)
Song, Kiburm (한양대학교 산업공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.38, no.2, 2013 , pp. 65-73 More about this Journal
Abstract
In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.
Keywords
Bootstrap Methods; Trended Time Series Data; Simulation Output Analysis;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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