Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?

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Abstract

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
JournalEconometrics
Volume5
Issue number4
DOIs
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

Keywords

  • Inflation
  • seasonal adjustment filters
  • mixed causal-noncausal models
  • TIME-SERIES
  • UNIT-ROOT TESTS
  • inflation

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