Identification of noncausal models by quantile autoregressions (with A Li Sun)
Abstract: We propose a model selection criterion to detect purely causal from noncausal models in the framework of quantile autoregression (QAR). We generalize the consistency result in the QAR to a generic case and the asymptomatic result to the case of i.i.d. regularly varying distributed innovations. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series. We illustrate our analysis using hyperination episodes in Latin American countries.
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