In spatial discrete choice models the spatial dependent structure adds complexity in the estimation of parameters. Appropriate general method of moments (GMM) estimation needs inverses of n-by-n matrices and an optimization complexity of the moment conditions for moderate to large samples makes...

Given the extreme dependence of agriculture on weather conditions, this paper analyses the effect of climatic variations on this economic sector, by considering both a huge dataset and a flexible spatio-temporal model specification. In particular, we study the response of N-fertilizer...

We propose a new spatio--temporal model with time--varying spatial weighting matrices. The filtering procedure of the time--varying unknown parameters is performed using the information contained in the score of the conditional distribution of the observables. We provide conditions for the...

We propose a new class of models specifi cally tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models...

Using data for most of the year 2020, we analysed the impact of COVID-19 deaths on a given country regarding the financial market returns of neighbouring countries. Our empirical evidence show that in the first weeks of the COVID-19 outbreak, until mid-March 2020, the spatial effect of COVID-19...