Individuals or companies in a large social or financial network often display rather
heterogeneous behaviors for various reasons. In this work, we propose a network vector …

This paper considers the identification of latent group structures in spatial dynamic panels.
We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model …

We consider network autoregressive models for count data with a non-random
neighborhood structure. The main methodological contribution is the development of …

We study the dynamic price competition of hotels in Venice using publicly available data
scraped from an online travel agency. This study poses two main challenges. First, the time …

This paper develops an asymptotic distribution theory for a two-stage instrumentation
estimation approach in quantile predictive regressions when both generated covariates and …

With the development of the internet, network data with replications can be collected at
different time points. The spatial autoregressive panel (SARP) model is a useful tool for …

We develop a variational Bayesian (VB) approach for estimating large-scale dynamic
network models in the network autoregression framework. The VB approach allows for the …

Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this
paper, we consider an integer‐valued network autoregressive model with a non‐random …

Motivated by a dataset of burglaries in Chicago, USA, we introduce a novel framework to
analyze time series of count data combining common multivariate time series models with …

We propose a dynamic network quantile regression model to investigate the quantile
connectedness using a predetermined network information. We extend the existing network …