Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195432 
Year of Publication: 
2017
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-22
Publisher: 
MDPI, Basel
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 has important implications for modelling economic time series driven by expectation relationships. We consider inflation data on the G7 countries to illustrate these results.
Subjects: 
inflation
seasonal adjustment filters
mixed causal-noncausal models
JEL: 
C22
E37
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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