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

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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.
Original languageEnglish
JournalEconometrics and Statistics
DOIs
Publication statusE-pub ahead of print - Mar 2022

JEL classifications

  • c22 - "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"

Keywords

  • MLE
  • Noncausal models
  • generalized Student's t-distribution
  • robust inference

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