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 TEL  Vol.9 No.8 , December 2019
Forecasting Economic Time Series in the Presence of Variance Instability and Outliers
Abstract: This work examines the impact of data transformation (for variance stabilization) and outlier adjustment (“linearization”) on the quality of univariate time series forecasts, considering each one separately, as well as in combination. Twenty of the most important time series of the Greek economy were used for this purpose. Empirical findings show a significant improvement in forecasts’ confidence intervals, but no substantial improvement in point forecasts. Furthermore, the combined transformation-linearization procedure improves substantially the non-normality problem encountered in many macroeconomic time series.
Cite this paper: Milionis, A. and Galanopoulos, N. (2019) Forecasting Economic Time Series in the Presence of Variance Instability and Outliers. Theoretical Economics Letters, 9, 2940-2964. doi: 10.4236/tel.2019.98182.
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