Identification of Noncausal Models by Quantile Autoregressions
Econometrics
2019-04-15 v1
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 in Latin American countries.
Cite
@article{arxiv.1904.05952,
title = {Identification of Noncausal Models by Quantile Autoregressions},
author = {Alain Hecq and Li Sun},
journal= {arXiv preprint arXiv:1904.05952},
year = {2019}
}