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Selecting between causal and noncausal models with quantile autoregressions

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

Studies in Nonlinear Dynamics & Econometrics, 2021, vol. 25, issue 5, 393-416

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.

Keywords: causal and noncausal time series; financial bubbles; model selection criterion; quantile autoregressions; regularly varying variables (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/snde-2019-0044

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