1,721,011 research outputs found
Three Essays on Model Choice and Estimation in Spatial Econometrics: Large-Sample Estimation, Isotropy Testing, and Multilevel Modelling
No full textLa nuova geografia della crescita
No full textIl presente volume nasce nell’ambito di un vasto progetto di ricerca sulle regioni alpine, sollecitato dalla Trento School of Management che, superando le visioni stereotipate della montagna come luogo marginale rispetto allo sviluppo, ha messo in luce le caratteristiche di territori che partecipano appieno della crescita del paese, come pure delle sue difficoltà e contraddizioni. L’affermazione dell’economia della conoscenza, ha cambiato in modo sostanziale le logiche della localizzazione offrendo nuove opportunità, ma al tempo stesso presentando nuove problematiche per i territori montani per i quali, oltre al venir meno delle condizioni di marginalità, sembra che sia anche leggibile una differente capacità di inserirsi nelle nuove condizioni dello sviluppo. L’idea di fondo del presente volume è di leggere lo sviluppo regionale nel decennio 2003-2012 alla luce dei modelli di convergenza/divergenza dei livelli di benessere (misurati dal PIL pro capite) delle regioni europee. All’interno di questa cornice è possibile dare una nuova lettura del ruolo dei fattori localizzativi sulla crescita, anche attraverso una analisi delle dinamiche settoriali di gruppi di aree con differente densità abitativa, e – al loro interno – delle aree alpine, tenendo anche conto delle reciproche influenze che tendono a stabilirsi tra economie di regioni geograficamente vicine
Finanza quantitativa con R
No full textIl libro tratta i principali temi della finanza quantitativa partendo dai concetti elementari, fino a toccare argomenti relativamente avanzati nell'ambito del pricing di strumenti finanziari e della misurazione del rischio. Lo scopo del testo è quello di presentare i risultati fondamentali della finanza quantitativa ed illustrarne l'applicazione a dati reali mediante il software statistico R. Il testo bilancia la trattazione teorica degli argomenti, con la presenza di esempi ampiamente ed approfonditamente discussi. Il livello del testo corrisponde a quello di un laboratorio di finanza quantitativa di un corso di laurea magistrale in finanza.The book deals with the main issues of quantitative finance, from the basis to more advanced topics on asset pricing and financial risk measurement. Illustrating the main theoretical results of quantitative finance and providing a method to apply them to real data are the goals of this book. The use of statistical software R is illustrated both from a general perspective, and through many detailed examples based on real data. The level of the treatment is suited for students of master’s degrees in finance
A mixed sampling strategy for partially geo-referenced finite populations
No full textIn the last few decades, sampling theory has been given a substantial boost by the growing availability of geo-referenced finite populations. Unfortunately, geo-referentiation is often incomplete or affected by locational errors for a portion of the units. Spatial sampling methods produce efficient estimates but suffer from consequences of flaws in geo-referentiation. This paper proposes a mixed sampling strategy for finite populations where a portion of the units is not correctly geo-referenced. The strategy exploits the available spatial information in the sampling design and adopts traditional sampling techniques for the remaining part of the population. Statistical properties of the strategy are explained and studied through Monte Carlo experiments on simulated and real data. An analysis of results in terms of efficiency and optimal sample composition is performed. The design-based nature of the proposed approach and its adaptability to several practical situations make it a general and easy-to-implement tool, which can outperform pure spatial sampling designs in terms of efficiency in estimation
Fitting Spatial Econometric Models through the Unilateral Approximation
No full textMaximum likelihood estimation of spatial models based on weight matrices typically requires a sizeable computational capacity, even in relatively small samples. The unilateral approximation approach to spatial models estimation has been suggested in Besag (1974) as a viable alternative to MLE for conditionally specified processes. In this paper we revisit the method, extend it to simultaneous spatial processes and study the finite-sample properties of the resulting estimators by means of Monte Carlo simulations, using several Conditional Autoregressive Models. According to the results, the performance of the unilateral estimators is very good, both in terms of statistical properties (accuracy and precision) and in terms of computing time.Maximum likelihood estimation of spatial models based on weight matrices typically requires a sizeable computational capacity, even in relatively small samples. The unilateral approximation approach to spatial models estimation has been suggested in Besag (1974) as a viable alternative to MLE for conditionally specified processes. In this paper we revisit the method, extend it to simultaneous spatial processes and study the finite-sample properties of the resulting estimators by means of Monte Carlo simulations, using several Conditional Autoregressive Models. According to the results, the performance of the unilateral estimators is very good, both in terms of statistical properties (accuracy and precision) and in terms of computing time
Tail analysis of a distribution by means of an inequality curve
No full textThe Zenga (1984) inequality curve λ(p) is constant in p for Type I Pareto distributions. We show that this property holds exactly only for the Pareto distribu- tion and, asymptotically, for distributions with power tail with index α, with α > 1. Exploiting these properties one can develop powerful tools to analyze and estimate the tail of a distribution. An estimator for α is discussed. Inference is based on an estimator of λ ( p) which utilizes all sample information for all values of p. The prop- erties of the proposed estimation strategy is analyzed theoretically and by means of simulations
Modelling and predicting the spatio-temporal spread of COVID-19 in Italy
Full text linkBACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.METHODS: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.RESULTS: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.CONCLUSIONS: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished
A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models
No full textIn this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online
A Cross-Entropy Approach to the Estimation of Generalised Linear Multilevel Models
Full text linkIn this paper we use the cross-entropy method for noisy optimisation for fitting generalised linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the paper, the method is used to model death probability of firms in the healthcare industry in Italy
Goodness-of-fit tests for Pareto and Log-normal distributions
No full textThe Zenga (1984) inequality curves λ(p) and Z(p) have some interesting properties. The former is constant in p for Type I Pareto distributions, while the latter is constant in p for Log- Normal distributions. After discussing in detail these aspects, these characterizing behaviors will be exploited to obtain graphical and analytical tools for tail analysis, estimation and goodness of fit tests. Order statistics-based estimators of the curves will be presented and discussed; furthermore, a testing procedure for Pareto-type behavior and one for Log- normality based on a regression of λ(p) and Z(p) against p will be introduced. The properties of the proposed estimation and testing strategies are analyzed theoretically and by means of simulations; comparisons with competing testing strategies are presented. An application to data sets on city sizes, facing the debated issue of distinguishing Pareto-type tails from Log-normal tails, illustrates how the proposed method works in practice
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