Das Kolloquium findet im Freihausgebäude der TU Wien (Adresse: Wiedner Hauptstraße 8-10, 1040 Wien) statt.

Unsere Vorträge werden über E-Mail ausgeschrieben. Um diese Ankündigungen zu erhalten senden Sie eine E-Mail an sympa@list.tuwien.ac.at mit dem Betreff "sub swm-kolloquium" um in die E-Mail-Liste eingefügt zu werden.

(Abmeldungen von dieser Liste sind jederzeit möglich, indem Sie eine E-Mail an sympa@list.tuwien.ac.at mit dem Betreff "unsub swm-kolloquium" senden.)

Upcoming Seminars

Past Seminars

March 5th 2025
Dr. Philipp Gersing, öffnet eine externe URL in einem neuen Fenster, University of Vienna
Title: A Distributed Lag Approach to the Generalised Dynamic Factor Model (GDFM)

Abstract: We provide estimation and inference for the Generalised Dynamic Factor Model (GDFM) under the assumption that the dynamic common component can be expressed in terms of a finite number of lags of contemporaneously pervasive factors. The proposed estimator is simply the OLS regression of the observed variables on estimated factors their lags. The factors are estimated with static principal components and therefore the estimator avoids frequency domain techniques entirely.

January 29th 2025
Prof. Kostantinos Fokianos, University of Cyprus
Title: Count Network Autoregression

Abstract: We study general nonlinear models for time series networks of integer valued data. The vector of high dimensional responses, measured on the nodes of a known network, is regressed non-linearly on its lagged value and on lagged values of the neighboring nodes by employing a smooth link function. We study stability conditions for such multivariate process and develop quasi maximum likelihood inference when the network dimension is increasing. In addition, we study linearity score tests by treating separately the cases of identifiable and non-identifiable parameters. In the case of identifiability, the test statistic converges to a chi-square distribution. When the parameters are not-identifiable, we develop a supremum-type test whose p-values are approximated adequately by employing a feasible bound and bootstrap methodology.