Modelling multivariate time series for count data

Abstract

The study of time series models for count data has become a topic of special interest during the last years. However, while research on univariate time series for counts now flourishes, the literature on multivariate time series models for count data is notably more limited. The main reason for this is that the analysis of multivariate counting processes presents many more difficulties. Specifically, the need to account for both serial and cross-correlation complicates model specification, estimation and inference. This thesis deals with the class of INteger-valued AutoRegressive (INAR) processes, a recently popular class of models for time series of counts. The simple, univariate INAR(1) process is initially extended to the 2-dimensional space. In this way, a bivariate (BINAR(1)) process is introduced. Subsequently, the time invariant BINAR(1) model is generalized to a BINAR(1) regression model. Emphasis is given on models with bivariate Poisson and bivariate negative binomial innovat ...
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DOI
10.12681/eadd/25175
Handle URL
http://hdl.handle.net/10442/hedi/25175
ND
25175
Alternative title
Μοντέλα πολυμεταβλητών χρονολογικών σειρών για διακριτά δεδομένα
Author
Pedeli, Xanthi
Date
2011
Degree Grantor
Athens University Economics and Business (AUEB)
Committee members
Καρλής Δημήτριος
Παυλόπουλος Χαράλαμπος
Κατσουγιάννη Κλεάνθη-Ελένη
Δελλαπόρτας Πέτρος
Ηλιόπουλος Γεώργιος
Φωκιανός Κωνσταντίνος
McCabe Brendan
Discipline
Social Sciences
Other Social Sciences
Keywords
BINAR; MINAR; Count time series; Autocorrelations; Cross-correlation; Poisson; Negative binomial distribution
Country
Greece
Language
English
Description
216 σ., im.
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