We develop a systematic framework for the joint modeling of returns and multiple daily
realized measures. We assume a linear state space representation for the log realized …

The requirements for the security of the network communication in critical infrastructures
have been more focused on the availability of the data rather than the integrity and the …

We propose convenient inferential methods for potentially nonstationary multivariate
unobserved components models with fractional integration and cointegration. Based on …

This volume presents original and up-to-date studies in unobserved components (UC) time
series models from both theoretical and methodological perspectives. It also presents …

We propose a setup for fractionally cointegrated time series which is formulated in terms of
latent integrated and short-memory components. It accommodates nonstationary processes …

This thesis develops generalizations of unobserved components and factor models to
account for long memory. Long memory describes a strongly persistent and often non …

We study the forecasting of the yearly outcome of the Boat Race between Cambridge and
Oxford. We compare the relative performance of different dynamic models for forty years of …

This thesis develops methods for conducting inference on nonlinear panel data models in
the presence of latent stochastic variables. We focus on developing likelihood-based …

This thesis studies the state space approach to time series analysis as a flexible framework
for modelling time-changing parameters in economics and finance. We focus on the …