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Selecting between causal and noncausal models with quantile autoregressions

Selecting between causal and noncausal models with quantile autoregressions AbstractWe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

Selecting between causal and noncausal models with quantile autoregressions

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Publisher
de Gruyter
Copyright
© 2020 Alain Hecq and Li Sun, published by De Gruyter, Berlin/Boston
ISSN
1558-3708
eISSN
1558-3708
DOI
10.1515/snde-2019-0044
Publisher site
See Article on Publisher Site

Abstract

AbstractWe 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.

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Dec 29, 2021

Keywords: causal and noncausal time series; financial bubbles; model selection criterion; quantile autoregressions; regularly varying variables

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