Search

Many international institutions working in the field of food policy have appealed to digitization as the cornerstone of future food system sustainability strategies. However, some works have cast doubts on an excessive emphasis on digitization benefits, highlighting some risk of negative side effects that would hamper, instead of facilitating, food system transition. By crossing the literature on the risks of digitization with that on power and sustainability, the paper stresses the importance of the concept of power for assessing the effects of digital technologies on food system sustainability. One conclusion is that to ensure that digitization is consistent with sustainability objectives, public intervention should address some critical issues, such as: data sovereignty, increased surveillance and corporate control over farming practices, increased influence of corporate power on state regulatory choices.

Ordinal responses in the form of ratings arise frequently in social sciences, marketing and business applications where preferences, opinions and perceptions play a major role. Main examples concern customers/users' satisfaction analysis where it is common to collect rater's evaluation on a hedonic scale, along with a set of covariates (both categorical and quantitative) that characterize the respondent and/or the item/service. In this framework, the ordinal nature of the response has to be properly taken into account when the interest is in the understanding of different response patterns in terms of subjects' covariates. In this spirit, a model-based tree procedure for ordinal scores is illustrated: its structure is based on a class of mixture models for ordinal rating data that implies a twofold analysis in terms of feeling and uncertainty and effective graphical visualization of results. The flexibility of the chosen modelling framework entails that the splitting criterion can be customized according to the purposes of the study and the available data, without disregarding uncertainty. Thus, the selection of variables yielding to the best partitioning results is driven by fitting measures or classical likelihood and deviance measurements, for instance. The contribution proposes to investigate the features of varying decision rules and thus implicitly addresses the problem of selecting the model-based tree that provides the most adequate and satisfying overview of response profiles. Comparison with alternative model-based approaches is also outlined.

Tree based methods in statistics are gaining a renewed interest in the Big Data era since they entail effective interpretation of results. In this setting, we apply a model-based technique to build trees for ordinal responses relying on a class of mixture models whose characteristic feature is the probabilistic specification of uncertainty. An application to the perception of happiness shows that the integration of tree methods with the chosen modelling boosts cluster analysis of respondents.

This study aims at analysing consumer behaviour towards online advertising, focusing on the attitude defined in terms of cognitive, affective and behavioural aspects. Likert scale was employed to capture these three aspects. However, subjective attitude is often a nebulous and vague feature to deal with. To this end, adopting a fuzzy clustering procedure, first a fuzzy coding of the levels of the Likert scale is adopted. Second, the Fuzzy k-Means clustering model for fuzzy data has been employed to segment a sample of web users. Two clusters have been identified that require different marketing communication strategies.

In this paper we propose a method to locate multiple structural breaks in financial time series that accounts for the interval structure of these series as well as for the presence of outliers. For each time unit, the upper and lower bound of the intervals depend on the closing value. Then, to locate the break dates, a robust exponential based distance, that is able to neutralize the impact of outlier, is employed in the framework of Atheoretical Regression Trees. An empirical application to the prices of an asset shows the usefulness of the proposed procedure.

In ogni paese moderno che si rispetti, si sa che la formazione e i saperi sono determinanti per il consolidamento della sfera pubblica democratica, per la crescita reale e per l’incremento dell’occupazione. L’art. 9 della Costituzione italiana stabilisce: “La Repubblica promuove lo sviluppo della cultura e la ricerca scientifica e tecnica”. Questo perché cultura e ricerca sono temi strettamente intrecciati: se non c’è alta formazione, se non c’è formazione tout court, non c’è sviluppo. Istruzione, saperi, università e ricerca, sono dunque fattori strategici per lo sviluppo economico del paese, per la creazione di posti di lavoro (più posti e più qualificati), per lo sviluppo della democrazia. Contribuiscono, inoltre, al raggiungimento di una maggiore consapevolezza dei diritti, in tutti i campi, da quello economico e sociale a quello civile, in altre parole, contribuiscono ad un aumento della cultura della legalità. In questi ultimi anni (almeno 10-15) governi di ogni colore, da Berlusconi a Prodi ... fino a Renzi, hanno perseguito, con una coerenza davvero inusuale per la politica italiana, un medesimo programma, anche se non esplicitamente dichiarato, di riforma dell’Università, quasi un pensiero unico sull’Accademia. Il risultato più evidente di questo programma, è che, in controtendenza con la maggior parte dei paesi avanzati ed emergenti, l’Italia ha disinvestito fortemente dall’università nel corso degli ultimi dieci anni.

Il capitolo analizza la dimensione lavorativa dell'integrazione in Italia. Il conseguimento di un adeguato grado di integrazione lavorativa non è limitabile al solo aspetto reddituale, ma anche ad altri aspetti ritenuti qualificanti: l'adeguatezza della posizione lavorativa, la valorizzazione del capitale umano e la continuità dell'attività svolta. Gli autori analizzano tali dimensioni definendo un apposito indicatore sintetico. Se viene confermata, da un lato, la forte differenza di genere nell'integrazione occupazionale raggiunta dagli stranieri, dall'altro lato viene dedicato ampio spazio all'analisi comparativa delle diverse nazionalità/aree di cittadinanza e alla discussione delle possibili implicazioni nei percorsi d'integrazione. In particolare, la comunità cinese si caratterizza per l'importanza dell'economia etnica e della imprenditorialità, che sembra essere la principale forma di mobilità professionale ascendente.

The detection of change points in time series is a popular subject of research and the most challenging task is to identify multiple changes occurring at unknown date. At this aim [2] proposed a method called ART (Atheoretical Regression Trees) that employs least squares regression trees to detect multiple breaks in the mean. In this paper we present a review of the method as well as two recent extensions meant to deal either with changes in the coefficients of a parametric model or with changes in time series imprecisely observed i.e. time ordered observations whose values are not known exactly, such as interval or ordinal time series. An empirical example concerning a continuous-valued imprecise time series is presented and discussed.

We propose a cluster analysis approach to quantify near-synonymy relations and compare non-parametric and parametric methods. The first approach is model free since it does not assume an underlying model of lexical knowledge but it uncovers the group structure in the set of near-synonyms of a target word by comparing the list of synonyms of the given entry with those of its near-synonyms as contained into available thesauri. Then, in order to validate the results provided by the cluster analysis, a statistical model is introduced for analyzing human judgments of perceived degree of synonymy, also by a relationship with subjects’ characteristics. Empirical evidence for a selected word of Italian is presented and discussed.

The analysis of structural-change models is nowadays a popular subject of research both in econometric and statistical literature. The most challenging task is to identify multiple breaks occurring at unknown dates. In case of multiple shifts in mean Cappelli and Reale (2005) have proposed a method called ART that employs regression trees to estimate the number and location of breaks. In this paper we focus on regime changes due to breaks in the coefficients of a parametric model and we propose an extension of ART that addresses this topic in the general framework of the linear model with multiple structural changes. The proposed approach considers in the tree growing phase the residuals of parametric models fitted to contiguous subseries obtained by splitting the original series whereas tree pruning together with model selection criteria provide the number of breaks. We present simulation results pertaining to the behavior of the proposed approach as well as an empirical application to the time series of the car drivers killed or seriously injured in Great Britain in traffic accidents aiming at evaluating the impact of UK introduction of compulsory seatbelt wearing.

The analysis of structural-change models is nowadays a popular subject of research both in econometric and statistical literature. The most challenging task is to identify multiple breaks occurring at unknown dates. In case of multiple shifts in mean Cappelli et al (2008) have proposed a method based on regression trees. In this paper we propose an extension of this method to address the problem of estimating the break dates and their number in the general framework of the linear model with multiple structural changes. We present simulation results pertaining to the behavior of the proposed approach.

In this paper we propose a procedure to detect the presence of co-breaking i.e. of a common structural break occurring at an unknown date in a vector of time series. Co-breaking occurs if a linear combination of the time series cancels the break. Our procedure employs a regression tree based approach called ART to detect the presence of breaks and Principal Component Analysis to generate the linear combinations of the vector series. On each of these linear combinations ART is performed again to detect the presence of a break. The combination that "hides" the co-breaking time is the one minimizing the employed splitting criterion. The results of a simulation study carried out to evaluate the performance of the proposed approach are presented and discussed.

In this paper we propose a procedure to detect the presence of co-breaking i.e. of a common structural break occurring at an unknown date in a vector of time series. Co-breaking occurs if a linear combination of the time series cancels the break. Our procedure employs a regression tree based approach namely ART, to detect the presence of breaks in the mean of each one-dimensional component of a multidimensional time series and Principal Component Analysis to estimate the linear combinations of the vector series. On each of these linear combinations ART is performed again to detect the presence of a break. The combination that "hides" the co-breaking time is the one minimizing the employed splitting criterion. The results of a simulation study carried out to evaluate the performance of the proposed approach are presented and discussed.

Obiettivo principale di questo capitolo è fornire un'immagine di sintesi sull'integrazione degli immigrati che vivono nel capoluogo campano e nei paesi vesuviani. A tal fine, dopo un escursus sulla letteratura italiana sul tema delle misure e degli indicatori di integrazione, viene proposto preliminarmente un quadro di sfondo attraverso il confronto della situazione nella provincia partenopea rispetto a quella rilevata nelle altre 31 unità territoriali coinvolte nell'indagine complessiva. L'analisi dell'integrazione culturale, sociale, politica ed economica degli stranieri presenti nella provincia partenopea viene condotta facendo ricorso a soluzioni tecnico-metodologiche differenti. Vengono infine esaminate le caratteristiche degli immigrati che maggiormente incidono sul livello di integrazione raggiunto, sia distintamente per le quattro dimensioni individuate sia con riferimento ad una loro sintesi. Proprio la ricerca di un indicatore/fattore di sintesi rappresenta un aspetto sul quale viene posta particolare attenzione. Evidente è lo svantaggio degli immigrati che vivono nel napoletano, particolarmente marcato con riferimento all'integrazione politico-economica. Ma anche all'interno del collettivo delle persone che vivono nelle due realtà della provincia partenopea si registrano differenze di non poco conto nel grado di integrazione raggiunto. Per tutte le dimensioni considerate, appare importante l'anzianità della presenza, a conferma del carattere processuale dell'integrazione che è il frutto di un percorso più o meno lungo e accidentato nel tempo. Va tenuto presente però che gli uomini, le persone meno giovani e meno istruite, nonché particolari gruppi nazionali, possono avere una minore propensione all'inserimento socio-culturale, così come variabili sono le possibilità offerte dai contesti locali di inserimento che si riflettono in special modo sui livelli di inserimento politico-economico. Peggiore risulta la situazione delle persone presenti nel polo dell'area metropolitana, dove minore è l'articolazione delle occasioni di lavoro e più complesso il mercato immobiliare, sia in locazione che in compravendita. Da non trascurare è infine il fatto che i diversi gruppi nazionali vadano ad assumere posizioni differenti sulle dimensioni socio-culturali e politico-economiche dell'integrazione. Al riguardo appare davvero esemplificativo e al contempo peculiare il caso dei Cinesi, fortemente indietro nel primo e nettamente avanti nel secondo ambito di inserimento. Si tratta di indicazioni che possono assumere una indubbia importanza nella definizione, articolazione e messa in campo di strategie e misure di intervento tese a fornire un effettivo supporto alla realizzazione di una effettiva società multietnica e multiculturale a bassa conflittualità.

La decisione viene vista come il risultato di una costruzione mentale determinata dal modo in cui ci si rappresenta il dilemma decisionale e si elaborano le informazioni che caratterizzano le diverse opzioni. La percezione o l'interpretazione della situazione come una minaccia può influire sulla rappresentazione del problema. L'ansia di tratto comporta alcuni biases valutativi, come un elevata percezione della minaccia, e una ridotta percezione del controllo. A centosette neo-diplomati che si erano recati all'Università di Salerno per chiedere informazioni inerenti la scelta del corso di laurea a cui iscriversi, è stato somministrato l'Endler Multidimensional Anxiety Scales (EMAS). A partire dall'ipotesi della multidimensionalità dell'ansia, abbiamo esplorato l'eventuale associazione esistente tra l'ansia di tratto, il grado di minaccia percepito e la rappresentazione immaginativa e verbale del problema decisionale mediante il metodo statistico dell'analisi delle corrispondenze multipleimmaginativa e verbale del problema decisionale mediante il metodo statistico della Analisi delle Corrispiondeze Multiple

In the last two decades a large amount of research both in the econometric and statistics literature has been concerned with the detection of structural changes in time series. The most challenging task is to identify multiple breaks occurring at unknown date and most contributions have addressed the case of level shifts. In this context Cappelli et al (2005, 2008) have proposed a computational efficient procedure called ART that employs regression trees to identify the breaks in the mean and their locations. In this streamline this paper focuses on a different problem: the detection of regime changes due to instability in model parameters. At this aim we propose an extension of the ART procedure that uses in the tree growing stage the residuals of models fitted to contiguous subseries obtained by splitting the original series. The best split is the one that maximizes the reduction in the residuals when splitting a node into its offsprings; the location of the splits provides the dates at which the regime change occurred. For the purpose to select of the final number of breaks the Chow test (1960) can be employed: splitting stops if the achieved reduction does not ensure the chosen significance level. Eventually, in order to circumvent the problem of model mispecification, in our programme various models can be assumed growing candidate trees (i.e., sets of breaks). The performance of the proposed approach is evaluated by means of a simulation study. An application to the US labor productivity index is also presented and discussed

In the last decade lot of interest, both in the econometric and statistical literature, has been paid to the issue of confusing long memory and structural breaks in level. Indeed, there is evidence that a stationary short memory process that encounters occasional level shifts can show a slow rate of decay in the autocorrelation function and other properties of I(d) processes, where d can be a fraction. On the other hand, when the data generating process is an integrated or fractionally integrated process (with no breaks), several breaks can be detected spuriously. Thus, testing procedures that allow a distinction between long memory and level shifts are currently a popular subject of research. Here we show the results of a simulation study on a simple empirical strategy that can be applied as a first check, to assess "what is what". This strategy consists in fitting long memory and structural break models separately; in case both provide plausible explanation of the DGP of the data at hand, the long memory and structural break analysis are repeated on the series break-free and on the filtered series, respectively. Then, the presence of breaks in the filtered series and/or of long memory behavior in the break-free series indicate whether the series shows real or spurious breaks and long memory.

In this paper we propose a strategy aimed at variable selection in models for ordinal data. The procedure core exploits the primary splitters, as they arise from a tree-based procedure. Indeed, the latter is based on a recursive partitioning approach that increasingly reduces the so called impurity of the response variable based on the covariate values. Since the reduction in impurity provides a natural importance ranking of the covariates, this allows to identify those relevant among the available ones. The proposed strategy is, then, used for detecting relevant covariates in a case study concerning the consumers’ preferences towards different types of smoked salmons.

ART was introduced as a modified use of CART methodology for quick detection of structural breaks in the mean levels. In this paper simulations are presented to test ART against a number of different types of time series, to find a good pruning method, and to compare with alternative approaches.

In this paper we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We propose a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of the Fisher grouping method (1958) proposed for grouping a single real variable. The proposed approach is applied to study the possibility of using the series of anomalous observation C17 provided by the seasonal adjustment procedure implemented in X12-ARIMA.

In this paper we propose a non parametric approach for detecting multiple structural break occurring at unknown dates. The method exploits, in the framework of least squares regression trees, the contiguity property introduced by Fisher for grouping a single real variable. The proposed approach called ART is applied to the structural breaks analysis of the U.S. Ex-Post Real Interest Rate series.

This paper proposes a non parametric approach for dating structural breaks whose number and dates of occurrence are a priori unknown. In particular, the case of level shifts is considered. For the purpose of locating the break-dates the method exploits, in the framework of least square regression trees, the contiguity property introduced by Fisher for grouping a single real variable. The proposal is applied to the study of the mean water level of Michigan Huron lake also comparing the results to those of the well known procedure recently proposed by Bai and Perron.