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3 décembre 2020
- 2012.01888
Mots-clés
Economics - EconometricsSujets proches
Induction, Ampliative Ampliative induction Inference (Logic)Citer ce document
Francesco Giancaterini et al., « Inference in mixed causal and noncausal models with generalized Student's t-distributions », arXiv - économie
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Résumé
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.