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Selecting between causal and noncausal models with quantile autoregressions

Author

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  • Hecq Alain
  • Sun Li

    (Maastricht University, School of Business and Economics, Department of Quantitative Economics, P.O.Box 616, 6200 MD Maastricht, The Netherlands)

Abstract

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.

Suggested Citation

  • Hecq Alain & Sun Li, 2021. "Selecting between causal and noncausal models with quantile autoregressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(5), pages 393-416, December.
    • Handle: RePEc:bpj:sndecm:v:25:y:2021:i:5:p:393-416:n:3
    DOI: 10.1515/snde-2019-0044
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    References listed on IDEAS

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    Cited by:

    1. Weifeng Jin, 2023. "Quantile Autoregression-based Non-causality Testing," Papers 2301.02937, arXiv.org.
    2. Alain Hecq & Li Sun, 2021. "Adaptive Random Bandwidth for Inference in CAViaR Models," Papers 2102.01636, arXiv.org.

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