Please click here if you are unable to view this page.
TOPIC:
REGRESSIONS WITH HEAVY TAILED WEAKLY NONSTATIONARY PROCESSES
ABSTRACT
We develop a limit theory for general additive functionals of Weakly Nonstationary Processes (WNPs) under heavy tailed innovations. In particular, we consider WNPs driven by innovations that are in the domain of attraction of an α-stable law with stability parameter α∈(0,2]. The current work generalises the recent limit theory of Duffy and Kasparis (2018), who consider WNPs under second moments. The defining characteristic of WNPs is that their empirical versions, upon standardisation, converge weakly to white noise processes rather than fractional Gaussian or fractional stable motions, which is typically the case under nonstationarity. As a consequence, the usual asymptotic methods (i.e. FCLTs) are not applicable, and different methods are required. The leading examples of WNPs under consideration are fractional d=1-1/α and mildly integrated processes driven by heavy tailed errors. Our main limit results are utilised for the asymptotic analysis of parametric and nonparametric regression estimators.