Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?

Alain Hecq, Sean Telg*, Lenard Lieb

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


This paper investigates the effect of seasonal adjustment filters on the identification of mixed causal-noncausal autoregressive models. By means of Monte Carlo simulations, we find that standard seasonal filters induce spurious autoregressive dynamics on white noise series, a phenomenon already
documented in the literature. Using a symmetric argument, we show that those filters also generate a spurious noncausal component in the seasonally adjusted series, but preserve (although amplify) the existence of causal and noncausal relationships. This result has important implications for modelling economic time series driven by expectation relationships. We consider inflation data on the G7 countries to illustrate these results.
Original languageEnglish
Article number48
Pages (from-to)1-22
Number of pages22
Issue number4
Publication statusPublished - 31 Oct 2017

JEL classifications

  • c22 - "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
  • e37 - Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications


  • Inflation
  • seasonal adjustment filters
  • mixed causal-noncausal models
  • inflation


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