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

Francesco Giancaterini; Alain Hecq

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arXiv · 2012.01888

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

Francesco Giancaterini·Alain Hecq

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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.

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BibTeX RIS

Francesco Giancaterini, Alain Hecq. 2020-12-03. Inference in mixed causal and noncausal models with generalized Student's t-distributions. https://arxiv.org/abs/2012.01888

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