The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Seasonal Adjustment

  • Svend Hylleberg
Reference work entry


The main objective of seasonally adjusted time series is to provide easy access to a common time series data-set purged of what is considered seasonal noise. Although the application of officially seasonally adjusted data may save costs, it may also imply less efficient use of the information available, and data may be distorted. Hence, in many cases, seasonality may need to be treated as an integrated part of an econometric analysis. In this article we present several ways to integrate seasonal adjustment into econometric analysis in addition to applying data adjusted by the two most popular adjustment methods.


ARIMA models ARMA models Autoregressive models Band spectrum regression Band-pass filters Basic structural model (BSM) Box–Jenkins model Cointegration Common seasonal features Evolving seasonals model Habit persistence HEGY test Henderson moving averages Jevons, W. Kalman filter Long memory Maximum likelihood Noise models Ordinary least squares (OLS) Periodic autoregressive model (PAR) Periodic cointegration Production smoothing Real business cycles Seasonal adjustment Seasonal cointegration Seasonal difference filter Seasonal dummies Time series models TRAMO/SEATS seasonal adjustment programme Transformations Univariate seasonal models Unobserved components (UC) models Vector autoregressions X-11 seasonal adjustment programme 

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The author is grateful for helpful comments from Niels Haldrup and Steven Durlauf.


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© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Svend Hylleberg
    • 1
  1. 1.