Spatial scan statistics are well-known methods for cluster detection and widely used in epidemiology medical studies detecting evaluating the statistical significance of disease hotspots. For sake simplicity, classical spatial statistic assumes that observations outcome variable different locations independent, while practice data may exhibit a correlation. In this article, we use autoregressiv...

Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional volatile time series. The available literature on such models is broad, but also sector-specific, overlapping, confusing. Most focus one property data, while much can be gained combining strength various their sources heterogeneity. We present a struct...

Recently, there has been a renewed interest in modeling economic time series by vector autoregressive moving-average models. However, this class of models has been unpopular in practice because of estimation problems and the complexity of the identification stage. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. In this paper, sev...

We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the genera...

This work develops a statistical model to assess the frost risk in Rafsanjan, one of the largest pistachio production regions in the world. These models can be used to estimate the probability that a frost happens in a given time-period during the year; a frost happens after 10 warm days in the growing season. These probability estimates then can be used for: (1) assessing the agroclimate risk ...