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In last years, the debate about social and economic development considered with increasing interest the exposure to the risk of poverty rather than poverty itself. The risk for an individual or a household to become or remain poor in an immediate future is defined as vulnerability to poverty. According to the recent literature, poverty is a complex phenomenon requiring an operationalisation that takes into account its multidimensionality, encompassing both objective and subjective elements. Due to the latent nature of poverty, it is possible to study this construct by analysing a set of manifest indicators. Focusing on vulnerability to poverty, a forward-looking perspective has also to be considered for depicting the dynamicity of the analysed phenomenon. For these reasons, here we propose to use latent transition analysis (LTA) to study vulnerability to poverty. This approach allows identifying unobservable (latent) classes within a population based on the responses to multiple observed variables. Moreover, it allows evaluating the movement between different classes over time, in terms of probability of transition. This probability can be used to estimate vulnerability to poverty. The usefulness of LTA in this context is showed by presenting a case study concerning Italian households over 2008–2012.

In high-dimensional portfolio selection, traditional asset allocation techniques often yield suboptimal results out-of-sample, while equally weighted portfolios have shown better performances in such scenarios. To leverage the advantages of diversification while addressing the curse of dimensionality, we turn to clustering techniques. Specifically, we explore the application of k -means clustering for time series, which offers a clear financial interpretation as the prototype of each cluster represents an equally weighted portfolio of the assets within the cluster. In this paper, we conduct a comprehensive comparison of various time series clustering techniques in the context of portfolio performance. By evaluating the out-of-sample performance of portfolios constructed using different clustering approaches, we aim to identify the most effective method for investment purposes.

In high-dimensional portfolio selection, traditional asset allocation techniques often yield suboptimal results out-of-sample, while equally weighted portfolios have shown better performances in such scenarios. To leverage the advantages of diversification while addressing the curse of dimensionality, we turn to clustering techniques. Specifically, we explore the application of k -means clustering for time series, which offers a clear financial interpretation as the prototype of each cluster represents an equally weighted portfolio of the assets within the cluster. In this paper, we conduct a comprehensive comparison of various time series clustering techniques in the context of portfolio performance. By evaluating the out-of-sample performance of portfolios constructed using different clustering approaches, we aim to identify the most effective method for investment purposes.

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

In recent years, the increasing number of natural disasters has raised concerns about the sustainability of our planet’s future. As young people comprise the generation that will suffer from the negative effects of climate change, they have become involved in a new climate activism that is also gaining interest in the public debate thanks to the Fridays for Future (FFF) movement. This paper analyses the results of a survey of 1,138 young people in a southern Italian region to explore their perceptions of the extent of environmental problems and their participation in protests of green movements such as the FFF. The statistical analyses perform an ordinal classification tree using an original impurity measure considering both the ordinal nature of the response variable and the heterogeneity of its ordered categories. The results show that respondents are concerned about the threat of climate change and participate in the FFF to claim their right to a healthier planet and encourage people to adopt environmentally friendly practices in their lifestyles. Young people feel they are global citizens, connected through the Internet and social media, and show greater sensitivity to the planet’s environmental problems, so they are willing to take effective action to demand sustainable policies from decision-makers. When planning public policies that will affect future generations, it is important for policymakers to know the demands and opinions of key stakeholders, especially young people, in order to plan the most appropriate measures, such as climate change mitigation.

Determining the spatial thickness (z) of in situ and reworked fallout pyroclastic deposits plays a key role in volcanological studies and in shedding light on geomorphological and hydrogeological processes in peri-volcanic areas. However, this is a challenging line of research because (1) field-based measurements are expensive and time-consuming, (2) the ash might have been dispersed in the atmosphere by several volcanic eruptions, and (3) wind characteristics during an eruptive event and soil-forming and/or denudation processes after ash deposition on the ground surface affect the expected spatial distribution of these deposits. This article tries to bridge this knowledge gap by applying statistical techniques for making representative spatial thickness predictions to be used for the analysis of geomorphic processes at the catchment and sub-catchment scales. First, we compiled a field-based thickness measurement dataset (https://doi.org/10.5281/zenodo.8399487; Matano et al., 2023) of fallout pyroclastic deposits in the territories of several municipalities in Campania, southern Italy. Second, 18 predictor variables were derived mainly from digital elevation models and satellite images and were assigned to each measurement point. Third, the stepwise regression (STPW) model and random forest (RF) machine learning technique are used for thickness modeling. Fourth, the estimations are compared with those of three models that already exist in the literature. Finally, the statistical combination of different predictions is implemented to develop a less biased model for estimating pyroclastic thickness. The results show that the prediction accuracy of RF (RMSE <82.46 and MAE <48.36) is better than that of existing models in the literature. Moreover, statistical combination of the predictions obtained from the above-mentioned models through a least absolute deviation (LAD) combination approach leads to the most representative thickness estimation (MAE <45.12) in the study area. The maps for the values estimated by RF and LAD (as the best single model and combination approach, respectively) illustrate that the spatial patterns did not change significantly, but the estimations by LAD are smaller. This combined approach can help in estimating the thickness of fallout pyroclastic deposits in other volcanic regions and in managing geohazards in areas covered with loose pyroclastic materials.

Along the coastline of the Phlegraean Fields volcanic district, near Naples (Italy), severe retreat processes affect a large part of the coastal cliffs, mainly made of fractured volcanic tuff and pyroclastic deposits. Progressive fracturing and deformation of rocks can lead to hazardous sudden slope failures on coastal cliffs. Among the triggering mechanisms, the most relevant are related to meteorological factors, such as precipitation and thermal expansion due to solar heating of rock surfaces. In this paper, we present a database of measurement time series taken over a period of ∼4 years (December 2014–October 2018) for the deformations of selected tuff blocks in the Coroglio coastal cliff. The monitoring system is implemented on five unstable tuff blocks and is formed by nine crackmeters and two tiltmeters equipped with internal thermometers. The system is coupled with a total weather station, measuring rain, temperature, wind and atmospheric pressure and operating from January 2014 up to December 2018. Measurement frequencies of 10 and 30 min have been set for meteorological and deformation sensors respectively. The aim of the measurements is to assess the magnitude and temporal pattern of rock block deformations (fracture opening and block movements) before block failure and their correlation with selected meteorological parameters. The results of a multivariate statistical analysis of the measured time series suggest a close correlation between temperature and deformation trends. The recognized cyclic, sinusoidal changes in the width (opening–closing) of fractures and tuff block rotations are ostensibly linked to multiscale (i.e., daily, seasonal and annual) temperature variations. Some trends of cumulative multi-temporal changes have also been recognized. The full databases are freely available online at: https://doi.org/10.1594/PANGAEA.896000 (Matano et al., 2018) and https://doi.org/10.1594/PANGAEA.899562 (Fortelli et al., 2019).

We present a long-range terrestrial laser scanner application for the geostructural mapping of Coroglio cliff, a tuff rock face exposed along the coastal zone of Campi Flegrei, Napoli. The procedure includes several phases (geomorphological analysis, structural field survey, laser scanner data acquisition and data processing, 3-D model development and analysis, geostructural classification of discontinuity orientation data and 2-D vertical cartographic production). Field data were processed with specific software dedicated to geostructural and geometric analysis. Spatial data were managed with a geographical information system and have been used for the construction of 2-D and 3-D geometric models of the rock cliff surface and geostructural interpretation. The main product of this study is a vertical geostructural map of the Coroglio cliff at 1:500 scale that illustrates the spatial distribution and orientation of the major families of structural discontinuities detected along the exposed surface of the cliff. The cartographic product includes base information useful to identify the main rock failure mechanisms along the cliff and represents a first step for the zonation of areas susceptible to block failures and the planning of monitoring activities.

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach in comparison with other proposed methods by performing CAD/USD exchange rate predictions.