Inference in mixed causal and noncausal models with generalized Student's t-distributions

Francesco Giancaterini and Alain Hecq

Papers from arXiv.org

Abstract: The properties of Maximum Likelihood estimator in mixed causal and noncausal models with a generalized Student's t error process are reviewed. Several known existing methods are typically not applicable in the heavy-tailed framework. To this end, a new approach to make inference on causal and noncausal parameters in finite sample sizes is proposed. It exploits the empirical variance of the generalized Student's-t, without the existence of population variance. Monte Carlo simulations show a good performance of the new variance construction for fat tail series. Finally, different existing approaches are compared using three empirical applications: the variation of daily COVID-19 deaths in Belgium, the monthly wheat prices, and the monthly inflation rate in Brazil.

Date: 2020-12, Revised 2022-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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Journal Article: Inference in mixed causal and noncausal models with generalized Student’s t-distributions (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2012.01888

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