Seasonality Shrinkage Procedures for Small Samples
Dan Williams illustrates the major statistical problems with seasonal indexes estimated from short time series. First it is difficult to distinguish the true seasonal index from the year-to-year random variation in the index. Second, the measured indexes are particularly sensitive to outliers in the data. Third, there is evidence that with short series, the indexes exaggerate the variation across seasons. Williams suggests that, when you lack a lengthy time series, you play it safe by shrinking (damping) the spread across the seasonal indexes and shows how this can be done. Copyright International Institute of Forecasters, 2007
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