[ABE-L] Fwd: Fw: [ESOBE] Maria Kalli and Jim Griffin Seminars at Ca' Foscari University of Venice - 3 November 2025

Sex Out 31 11:19:19 -03 2025

------------------------------
*From:* esobe <esobe-bounces em wu.ac.at> on behalf of Roberto CASARIN <
r.casarin em unive.it>
*Sent:* Friday, October 31, 2025 07:11
*To:* esobe em wu.ac.at <esobe em wu.ac.at>
*Subject:* [ESOBE] Maria Kalli and Jim Griffin Seminars at Ca' Foscari
University of Venice - 3 November 2025

Dear Colleagues,
I'm pleased to invite you to the Department Seminars, which will be held on
Monday, November 3 at 11:00 in Meeting Room 1 with the following schedule:

- 11:00-12:00: *Maria Kalli *(*King's College London*), Network Modeling of
Asynchronous Change-Points in Multivariate Time Series

- 12.15- 13.15: *Jim Griffin* (*University College London*), Some
approaches to modelling high-dimensional multivariate time series

All the interested researchers are warmly invited to attend in person or
online!

Best regards
Roberto Casarin (and on behalf of the Organizing Committee)

More information and Zoom link are available here:
https://www.unive.it/data/agenda/1/104528
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https://unive.zoom.us/j/81247640154?pwd=rPM81OcUZRb5VIW4NSHwOPLurD01le.1
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*Title*: Network Modeling of Asynchronous Change-Points in Multivariate
Time Series
*Abstract *: We introduce a novel Bayesian method for asynchronous
change-point detection in multivariate time series. This method allows for
change-points to occur earlier in some (leading) series followed, after a
short delay, by change-points in some other (lagging) series. Such dynamic
dependence structure is common in fields such as seismology and neurology
where a latent event such as an earthquake or seizure causes certain
sensors to register change-points before others. We model these lead-lag
dependencies via a latent directed graph and provide a hierarchical prior
for learning the graph’s structure and parameters. Posterior inference is
made tractable by modifying particle MCMC methods designed for univariate
change-point problems. We apply our method to both simulated and real
datasets from the fields of seismology and neurology. In the simulated
data, we find that our method outperforms competing methods in settings
where the change-point locations are dependent across series. In the real
data applications we show that our model can also uncover an interpretable
network structure.

*Title*: Some approaches to modelling high-dimensional multivariate time
series
*Abstract*: There has been an increasing interest in modelling
high-dimensional multivariate economic time series. Many models build on
the work-horse Vector AutoRegression (VAR) and its time-varying extension
to TVP-VAR. These models can provide better forecasts and structural
analysis than low-dimensional models (particularly during crisis periods)
but the large number of parameters can be challenging both inferentially
and computationally. In this talk, I will review two recent approaches. The
first is the Tensor VAR (TVAR) model which uses a tensor structure to
achieve dimension reduction in the coefficient matrices of the VAR. I will
discuss Bayesian inference in these models and an extension to a
time-varying parameter model. The second approach considers the
time-varying Factor Augmented VAR (FA-VAR) and uses an autoencoder to
extract low-dimensional non-linear factors from high-dimensional data. I
will discuss how a shrinkage prior using groupings of the variables can
lead to identifiable factors and better predictive performance.

--
Roberto Casarin, PhD
Professor of Econometrics
Ca' Foscari University of Venice
San Giobbe 873/b - 30121 Venezia, Italy
http://sites.google.com/view/robertocasarin/
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https://www.unive.it/vera
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https://www.unive.it/isba2024
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