Does the Box-Cox transformation help in forecasting macroeconomic time series?

By Tommaso Proietti, Helmut Lütkepohl. 2013

38 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Konstantinos Nikolopoulos
2. Evangelos Spiliotis
3. Vassilios Assimakopoulos
4. Fotios Petropoulos
5. Rob J Hyndman
6. Spyros Makridakis
7. Boris N. Oreshkin
8. Rafal Weron

In Management Science and Operations Research

1. Evangelos Spiliotis
2. Konstantinos Nikolopoulos
3. Vassilios Assimakopoulos
4. Fotios Petropoulos
5. Spyros Makridakis
6. Rob J Hyndman
7. Alexei Botchkarev
8. Slawek Smyl

Citations in the corpus

• Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors (2018, background): “…et al. (2014) argue that temporal aggregated series of low frequencies can be used for identifying long-term characteristics of the data, while Proietti and Lütkepohl (2013) suggest the use of power transformations for rescaling the historical data, therefore simplifying their patterns and…”