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http://hdl.handle.net/2123/8167
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| Title: | Does the Box-Cox transformation help in forecasting macroeconomic time series? |
| Authors: | Proietti, Tommaso Lütkepohl, Helmut Discipline of Business Analytics |
| Keywords: | Forecasts comparisons Multi-step forecasting Rolling forecasts Nonparametric estimation of prediction error variance |
| Issue Date: | Oct-2011 |
| Publisher: | Business Analytics. |
| Series/Report no.: | OMEWP 08/2011 |
| Abstract: | The paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the Box-Cox power transformation, which applies to series measured on a ratio scale. We propose a nonparametric approach for estimating the optimal transformation parameter based on the frequency domain estimation of the prediction error variance, and also conduct an extensive recursive forecast experiment on a large set of seasonal monthly macroeconomic time series related to industrial production and retail turnover. In about one fifth of the series considered the Box-Cox transformation produces forecasts significantly better than the untransformed data at one-step-ahead horizon; in most of the cases the logarithmic transformation is the relevant one. As the forecast horizon increases, the evidence in favour of a transformation becomes less strong. Typically, the naïve predictor that just reverses the transformation leads to a lower mean square error than the optimal predictor at short forecast leads. We also discuss whether the preliminary in-sample frequency domain assessment conducted provides a reliable guidance which series should be transformed for improving significantly the predictive performance. |
| URI: | http://hdl.handle.net/2123/8167 |
| Department/Unit/Centre: | Discipline of Business Analytics |
| Appears in Collections: | Working Papers - Business Analytics |
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