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dc.contributor.authorProietti, Tommaso
dc.contributor.authorLütkepohl, Helmut
dc.date.accessioned2012-03-09T02:59:02Z
dc.date.available2012-03-09T02:59:02Z
dc.date.issued2011-10
dc.identifier.urihttp://hdl.handle.net/2123/8167
dc.description.abstractThe 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.en_AU
dc.language.isoenen_AU
dc.publisherBusiness Analytics.en_AU
dc.relation.ispartofseriesBAWP-2011-08en_AU
dc.subjectForecasts comparisonsen_AU
dc.subjectMulti-step forecastingen_AU
dc.subjectRolling forecastsen_AU
dc.subjectNonparametric estimation of prediction error varianceen_AU
dc.titleDoes the Box-Cox transformation help in forecasting macroeconomic time series?en_AU
dc.typeWorking Paperen_AU
dc.contributor.departmentDiscipline of Business Analyticsen_AU


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