Essays in Robust and Nonlinear Time Series Models.

D'Innocenzo, Enzo (2020) Essays in Robust and Nonlinear Time Series Models., [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Scienze statistiche, 32 Ciclo. DOI 10.6092/unibo/amsdottorato/9328.
Documenti full-text disponibili:
[img] Documento PDF (English) - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato.
Download (6MB)

Abstract

This PhD dissertation deals with the world of multivariate time series models where the behaviour of the observed process is described by using a time-varying parameter. In particular, this thesis explore three different dynamic multivariate nonlinear models which are able to deal with multivariate time series gathered from heavy-tailed phenomena. Although the popularity of linear and univariate time series models, empirical evidences have shown that variables generated from complex phenomena are typically inter-related both contemporaneously and across time. This is the case for several fields of science such as economics, finance, biology or physics, where it is widely accepted that with a univariate approach it is difficult to obtain a satisfactory representation of the reality or to make good predictions about the future. For these reasons, the literature of linear multivariate Gaussian time series models has received increasing attention. However, these models are known for their unsatisfactory performances when the collected data are contaminated by outliers, yielding biased estimates and unreliable forecasts. In fact, when departure from the hypothesis of normality is confirmed by the observed data, it is reasonable to switch into the realm of nonlinear or non-Gaussian time series models. Unfortunately, despite the development of recent technologies, the estimation of nonlinear time series models might be really challenging, since they require simulation-based and computer-intensive methods. In addition, statistical properties of such estimators are not always easy to be derived. This thesis contributes to the literature by defining dynamic multivariate and heavy-tailed models that are relatively simple. The emphasis is models which are analytically tractable and can be easily estimated by means of maximum likelihood. For each of the models, a very detailed statistical and asymptotic analysis it is provided. Their practical usefulness is highlighted with several simulation studies and empirical applications.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
D'Innocenzo, Enzo
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
32
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Nonlinear Time Series Models, Robust Filtering, Score-Driven Models.
URN:NBN
DOI
10.6092/unibo/amsdottorato/9328
Data di discussione
2 Aprile 2020
URI

Altri metadati

Statistica sui download

Gestione del documento: Visualizza la tesi

^