Selecting between causal and noncausal models with quantile autoregressions

A. Hecq, L. Sun*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


We propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.
Original languageEnglish
Pages (from-to)393-416
Number of pages24
JournalStudies in Nonlinear Dynamics and Econometrics
Issue number5
Publication statusPublished - 29 Dec 2021


  • causal and noncausal time series
  • financial bubbles
  • model selection criterion
  • quantile autoregressions
  • regularly varying variables

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