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A study of the effect of data transformation and «linearization» on time series forecasts. A practical approach

Author

Listed:
  • Alexandros E. Milionis

    (Bank of Greece and University of the Aegean)

  • Nikolaos G. Galanopoulos

    (Bank of Greece (Trainee) and University of the Aegean)

Abstract

Very often in actual macroeconomic time series there are causes that disrupt the underlying stochastic process and their treatment is known as «linearization». In addition, variance non-stationarity is in many cases also present in such series and is removed by proper data transformation. The impact of either (data transformation - linearization) on the quality of forecasts has not been adequately studied to date. This work examines their effect on univariate forecasting considering each one separately, as well as in combination, using twenty of the most important time series for the Greek economy. 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.

Suggested Citation

  • Alexandros E. Milionis & Nikolaos G. Galanopoulos, 2020. "A study of the effect of data transformation and «linearization» on time series forecasts. A practical approach," Working Papers 280, Bank of Greece.
    • Handle: RePEc:bog:wpaper:280
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    References listed on IDEAS

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    1. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
    2. Alexandros E. Milionis & Nikolaos G. Galanopoulos, 2018. "Time series with interdependent level and second moment: statistical testing and applications with Greek external trade and simulated data," Working Papers 246, Bank of Greece.
    3. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    4. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    5. Alexandros Milionis, 2004. "The importance of variance stationarity in economic time series modelling. A practical approach," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 265-278.
    6. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    7. Nelson, Harold Jr. & Granger, C. W. J., 1979. "Experience with using the Box-Cox transformation when forecasting economic time series," Journal of Econometrics, Elsevier, vol. 10(1), pages 57-69, April.
    8. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
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    Cited by:

    1. Alexandros E. Milionis & Nikolaos G. Galanopoulos & Peter Hatzopoulos & Aliki Sagianou, 2022. "Forecasting actuarial time series: a practical study of the effect of statistical pre-adjustments," Working Papers 297, Bank of Greece.

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    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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