In this work a general semi-parametric multivariate model where the first two conditional
moments are assumed to be multivariate time series is introduced. The focus of the estimation is …

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 …

We propose a novel estimation approach for a general class of semi-parametric time series
models where the conditional expectation is modeled through a parametric function. The …

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

We study general nonlinear models for time series networks of integer and continuous-valued
data. The vector of high-dimensional responses, measured on the nodes of a known …

A quasi-score linearity test for continuous and count network autoregressive models is
developed. We establish the asymptotic distribution of the test when the network dimension is …

We develop methodology for network data with special attention to epidemic network spatio-temporal
structures. We provide estimation methodology for linear network autoregressive …

Statistical inference for discrete-valued time series has not been developed as systematically
as traditional methods for time series generated by continuous random variables. This Ph.D…

Statistical inference for discrete-valued time series has not been developed like traditional
methods for time series generated by continuous random variables. Some relevant models …

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