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Intervention analysis based on exponential smoothing methods: Applications to 9/11 and COVID-19 effects

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  • Seong, Byeongchan
  • Lee, Kiseop

Abstract

This study extends intervention analysis beyond the ARIMA models, which are currently used by most scholars and practitioners, to exponential smoothing models. This allows us to obtain the benefits of exponential smoothing modeling in analyzing time series with interventions. Exponential smoothing modeling allows for easier seasonal adjustments, and complex seasonality can be more readily incorporated into the analysis. In this study, we propose a method of intervention analysis based on exponential smoothing models through an innovational state-space model, and we obtain maximum likelihood estimates by maximizing the likelihood function of the state-space model. We analyze two applications: the 9/11 effect on U.S. airlines and the COVID-19 effect on the current population of Seoul, Korea. From the proposed method, we estimate the intervention effects and seasonal components in each series. This results in seasonally-adjusted time series with both intervention and seasonality removed.

Suggested Citation

  • Seong, Byeongchan & Lee, Kiseop, 2021. "Intervention analysis based on exponential smoothing methods: Applications to 9/11 and COVID-19 effects," Economic Modelling, Elsevier, vol. 98(C), pages 290-301.
    • Handle: RePEc:eee:ecmode:v:98:y:2021:i:c:p:290-301
    DOI: 10.1016/j.econmod.2020.11.014
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    References listed on IDEAS

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    Cited by:

    1. Ftiti, Zied & Ben Ameur, Hachmi & Louhichi, Waël, 2021. "Does non-fundamental news related to COVID-19 matter for stock returns? Evidence from Shanghai stock market," Economic Modelling, Elsevier, vol. 99(C).

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    More about this item

    Keywords

    Innovational state-space model; Seasonal adjustment; Holt-Winters method; Air traffic passenger miles; Current population;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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

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