1,720,971 research outputs found
Gender differences in the perception of inflation
No full textUsing data from Italy (1994–2018), we investigate gender differences in consumers’ inflation perceptions over time. We introduce a dynamic model in order to detect the changes in the shape of the probability distributions of judgments across time and to compare the behavior of the two groups of respondents. The model components describe the deep conviction of respondents about past inflation and the uncertainty generated by the intrinsic fuzziness surrounding the evaluation process. The results suggest that women tend to perceive a higher level of inflation than men, but this propensity has changed over the years. The Euro changeover and other economic events produced an increase in the heterogeneity of men’s responses and decreased the gap between the feelings of men and women about inflation. When the perceived inflation closely tracked the true rate, the gender difference was more pronounced because of the smaller heterogeneity and the higher asymmetry in the distribution of women’s judgments
A statistical procedure for representing state fragility and transition paths
No full textState fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks
“Detecting Semiotically Expressed Humor in Diasporic TV Productions”
No full textIn this article, we suggest a semiotic approach to the study of visual humorous texts. Our method is based on the multimodal script analysis, which is a useful tool for examining not only verbal texts but also more complex texts, which combine the presence of images and sounds with verbally expressed humor. The resulting framework highlights how some visual comic mecha- nisms may enhance a di¤erent perception of semiotically expressed humor. Moreover, we present a statistical model in order to detect and measure how the resolution of some incongruities may also be determined by specific variables, which help to establish the existence and the strength with which the appreciation of humor varies according to the ethnic group of origin. In particular, the study analyzes the clip ‘Jodhpur Station, 1947’ from a very popular British Asian sketch-show, Goodness Gracious Me (GGM). The sketch shares some similar features with the narrative strategies typical of joke-tellers and is characterized by a complex humorous apparatus depending on different levels of understanding relating to encyclopedic, cross-cultural, and even diasporic knowledge of the world
Joint modelling of ordinal data: a copula based approach
No full textIn this article we present an innovative technique to construct a multivariate distribution from margins described by CUB models. In particular, we use the Plackett distribution as a copula function, and we apply the discrete vine pair copula construction method to achieve a computational efficient solution. The proposed approach will be applied to model the importance of three key drivers of extra-virgin oil consumption in Italy
Comparing multistep ahead forecasting functions for time series clustering
No full textThe autoregressive metric between ARIMA processes has been originally introduced as the Euclidean distance between the AR weights of the one-step-ahead forecasting functions. This article proposes a novel distance criterion between time series that compares the corresponding multistep ahead forecasting functions and that relies on the direct method for model estimation. The proposed approach is complemented by a strategy for visual exploration and clustering based on the DISTATIS algorithm
The Discursive Representation of Smart Cities in the German Media
No full textThe aim of this paper is to investigate how German newspapers construct the imagery of smart cities and how they contribute to the delineation of the future city identity describing the set of unique abilities needed to manage the spatial, economic, social and cultural lives of German citizens. In particular, an integrated approach is applied. This combines a statistical method with corpus analysis in order to examine how newspapers disseminate and popularise scientific knowledge about smart cities and how they shape public opinion and promote the comprehension and acceptance of innovations among their readers
A Dynamic Model for Ordinal Time Series: An Application to Consumers’ Perceptions of Inflation
No full textThis article discusses an innovative model for time series ordinal data, which develops the well-established CUB model to allow for time-varying parameters. This is a mixture of a Uniform and a Shifted Binomial distribution, characterized by two parameters that can be interpreted as a measure of the ability of the rater to use the available rating scale and the degree of liking/disliking about the item. For illustrative purposes, the method is applied to consumers’ perceptions of inflation in Italy
Inflation Perceptions and Expectations During the Pandemic: A Model Based Approach
No full textThis article investigates how consumers’ perceptions and expectations about inflation evolved during the first twelve months after the pandemic broke out in Italy. The analysis is based on data from the European business and consumer qualitative surveys and exploits an innovative dynamic model for ordinal data based on a mixture distribution with time varying parameters
A multi-step approach for streamflow classification
No full textThe article presents a strategy for classifying streamflows into groups based on their temporal dynamics. It compares dynamic patterns of river discharges over time, capturing seasonality and short-term components using linear time series models. The final classification offers a means of identifying hydrological regimes and gives a preliminary understanding of the potential impacts of climate change on streamflows. The approach is illustrated by analyzing streamflow data from 221 stations in the United States in two non overlapping periods
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