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Forecasting power-transformed time series data

Author

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  • Paul De Bruin
  • Philip Hans Franses

Abstract

When there is an interest in forecasting the growth rates as well as the levels of a single macro-economic time series, a practitioner faces the question of whether a forecasting model should be constructed for growth rates, for levels, or for both. In this paper, we investigate this issue for 10 US (un-)employment series, where we evaluate the forecasts from a non-linear time series model for power-transformed data. Our main finding is that models for growth rates (levels) do not automatically result in the most accurate forecasts of growth rates (levels).

Suggested Citation

  • Paul De Bruin & Philip Hans Franses, 1999. "Forecasting power-transformed time series data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 807-815.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:807-815
    DOI: 10.1080/02664769922043
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    References listed on IDEAS

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    1. Franses, Philip Hans & de Bruin, Paul, 2002. "On data transformations and evidence of nonlinearity," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 621-632, September.
    2. Skalin, Joakim & Teräsvirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(2), pages 202-241, April.
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    Cited by:

    1. Proietti, Tommaso & Lütkepohl, Helmut, 2013. "Does the Box–Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 88-99.
    2. Kiygi Calli, M. & Weverbergh, M. & Franses, Ph.H.B.F., 2010. "To Aggregate or Not to Aggregate: Should decisions and models have the same frequency?," ERIM Report Series Research in Management ERS-2010-046-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Key-Il Shin & Hee-Jeong Kang, 2001. "A study on the effect of power transformation in the ARMA(p,q) model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(8), pages 1019-1028.
    4. Corradi, Valentina & Swanson, Norman R., 2006. "The effect of data transformation on common cycle, cointegration, and unit root tests: Monte Carlo results and a simple test," Journal of Econometrics, Elsevier, vol. 132(1), pages 195-229, May.
    5. Kiygi-Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2017. "Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 90-101.

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