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A random forest-based approach to identifying the most informative seasonality tests

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  • Ollech, Daniel
  • Webel, Karsten

Abstract

Virtually each seasonal adjustment software includes an ensemble of seasonality tests for assessing whether a given time series is in fact a candidate for seasonal adjustment. However, such tests are certain to produce either the same resultor conflicting results, raising the question if there is a method that is capable of identifying the most informative tests in order (1) to eliminate the seemingly non-informative ones in the former case and (2) to find a final decision in the more severe latter case. We argue that identifying the seasonal status of a given time series is essentially a classification problem and, thus, can be solved with machine learning methods. Using simulated seasonal and non-seasonal ARIMA processes that are representative of the Bundesbank's time series database, we compare certain popular methods with respect to accuracy, interpretability and availability of unbiased variable importance measures and find random forests of conditional inference trees to be the method which best balances these key requirements. Applying this method to the seasonality tests implemented in the seasonal adjustment software JDemetra+ finally reveals that the modifiedQSand Friedman tests yield by far the most informative results.

Suggested Citation

  • Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdps:552020
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    Cited by:

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    6. Bogdan Oancea & Richard Pospíšil & Marius Nicolae Jula & Cosmin-Ionuț Imbrișcă, 2021. "Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods," Mathematics, MDPI, vol. 9(19), pages 1-17, October.
    7. Ollech, Daniel, 2021. "Economic analysis using higher frequency time series: Challenges for seasonal adjustment," Discussion Papers 53/2021, Deutsche Bundesbank.
    8. Ersin Sünbül, 2023. "Linear and Nonlinear Relationship Between Real Exchange Rate, Real Interest Rate and Consumer Price Index: An Empirical Application for Countries with Different Levels of Development," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 70(1), pages 57-70, March.

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    More about this item

    Keywords

    binary classification; conditional inference trees; correlated predictors; JDemetra+; simulation study; supervised machine learning;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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