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Identification of Noncausal Models by Quantile Autoregressions

  • KanKan
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Document pages: 32 pages

Abstract: We propose a model selection criterion to detect purely causal from purelynoncausal models in the framework of quantile autoregressions (QAR). We alsopresent asymptotics for the i.i.d. case with regularly varying distributedinnovations in QAR. This new modelling perspective is appealing forinvestigating the presence of bubbles in economic and financial time series,and is an alternative to approximate maximum likelihood methods. We illustrateour analysis using hyperinflation episodes in Latin American countries.

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