Parameter estimation in Autoregressive network models

Parameter estimation in Autoregressive network models Cover image

In network time series, with errors correlated through the network at every time step but also correlated across time, estimating contemporaneous autocorrelations and those over time is a substantial challenge. Yet, for applications, understanding scenarios which guarantee consistent parameter estimation and scenarios in which some of these parameters cannot be consistently estimated, is needed to reach valid conclusions from data.

 

Attacking this problem requires both theoretical expertise and a range of suitable data sets for performance assessment. The project includes interactions with Financial NetworksAnalytics, with a particular emphasis on models for networks between banks in which correlations arise for example through inter-bank lending

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Autoregressive network models with stylized features of network data

We will propose several dynamic network models based on a simple AR(1) network framework. The setting depicts the dynamic changes for network edges e

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Modelling and forecasting dynamic networks via their edges

Networks that arise in fields such as biology or energy present features that challenge established modelling setups since the target function may nat

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