239 research outputs found

    Learning from experts: energy efficiency in residential buildings

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    Measuring and reducing energy consumption constitutes a crucial concern in public policies aimed at mitigating global warming. The real estate sector faces the challenge of enhancing building efficiency, where insights from experts play a pivotal role in the evaluation process. This research employs a machine learning approach to analyze expert opinions, seeking to extract the key determinants influencing potential residential building efficiency and establishing an efficient prediction framework. The study leverages open Energy Performance Certificate databases from two countries with distinct latitudes, namely the UK and Italy, to investigate whether enhancing energy efficiency necessitates different intervention approaches. The findings reveal the existence of non-linear relationships between efficiency and building characteristics, which cannot be captured by conventional linear modeling frameworks. By offering insights into the determinants of residential building efficiency, this study provides guidance to policymakers and stakeholders in formulating effective and sustainable strategies for energy efficiency improvement

    Sustainable finance: a journey toward ESG and climate risk

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    The present paper proposes an overview of the existing literature covering several aspects related to environmental, social, and governance (ESG) factors. Specifically, we consider studies describing and evaluating ESG methodologies and those studying the impact of ESG on credit risk, debt and equity costs, or sovereign bonds. We further expand the topic of ESG research by including the strand of the literature focusing on the impact of climate change on financial stability, thus allowing us to also consider the most recent research on the impact of climate change on portfolio management

    Secondary infections worsen the outcome of COVID-19 in patients with hematological malignancies a report from the ITA-HEMA-COV

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    The impact of secondary infections (SI) on COVID-19 outcome in patients with hematological malignancies (HM) is scarcely documented. To evaluate incidence, clinical characteristics, and outcome of SI, we analyzed the microbiologically documented SI in a large multicenter cohort of adult HM patients with COVID-19. Among 1741 HM patients with COVID-19, 134 (7.7%) had 185 SI, with a 1-month cumulative incidence of 5%. Median time between COVID-19 diagnosis and SI was 16 days (IQR: 5-36). Acute myeloid leukemia (AML) and lymphoma/plasma cell neoplasms (PCN) were more frequent diagnoses in SI patients compared to patients without SI (AML: 14.9% vs 7.1%; lymphoma /PCN 71.7% vs 65.3%). Patients with SI were older (median age 70 vs 66 yrs, p=0.002), with more comorbidities (median Charlson Comorbidity Index 5 vs 4, p<0.001), higher frequency of critical COVID-19 (19.5% vs 11.5%, p=0.046), and more frequently not in complete remission (75% vs 64.7% p=0.024). Blood and bronchoalveolar lavage were the main sites of isolation for SI. Etiology of infections was bacterial in 80% (n=148) of cases, mycotic in 9.7% (n=18) and viral in 10.3% (n=19); polymicrobial infections were observed in 24 patients (18%). Escherichia coli represented most of Gram-negative isolates (18.9%), while coagulase-negative Staphylococci were the most frequent among Gram-positive (14.2%). The 30-days mortality of patients with SI was higher when compared to patients without SI (69% vs 15%, p<0.001). The occurrence of SI worsened COVID-19 outcome in HM patients. Timely diagnosis and adequate management should be considered to improve their prognosis. This article is protected by copyright. All rights reserved

    High-Dimensional Radial Symmetry of Copula Functions: Multiplier Bootstrap vs. Randomization

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    We use a recently proposed fast test of copula radial symmetry based on multiplier bootstrap and obtain an equivalent randomization test. The literature shows the statistical superiority of the randomization approach in the bivariate case. We extend the comparison of statistical performance focusing on the high-dimensional regime in a simulation study. We document radial asymmetry in the joint distribution of the percentage changes of sectorial industrial production indices of the European Union

    Forecasting Economic Indicators with Robust Factor Models

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    Outliers can cause significant errors in forecasting, and it is essential to reduce their impact without losing the information they store. Information loss naturally arises if observations are dropped from the dataset. Thus, two alternative procedures are considered here: the Fast Minimum Covariance Determinant and the Iteratively Reweighted Least Squares. The procedures are used to estimate factor models robust to outliers, and a comparison of the forecast abilities of the robust approaches is carried out on a large dataset widely used in economics. The dataset includes observations relative to the 2009 crisis and the COVID-19 pandemic, some of which can be considered outliers. The comparison is carried out at different sampling frequencies and horizons, in-sample and out-of-sample, on relevant variables such as GDP, Unemployment Rate, and Prices for both the US and the EU

    Creditworthiness and Buildings’ Energy Efficiency in the Mortgage Market

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    Energy efficiency represents one of the key planned actions aiming to reduce greenhouse emissions and the consumption of fossil fuels in order to mitigate the impact of climate change. According to the European Commission1, buildings represent 40% of the total energy consumption and are responsible for 36% of the total CO2 emissions in Europe. Currently, only 25% of the stock of European buildings is considered energy efficient. In the 2030 climate and energy framework, the European Commission has set as a minimum target for the improvement in energy efficiency by 32.5%. The process of making buildings energy efficient reveals several advantages in terms of economic stimuli (e.g. the construction of buildings is a driving force component of the GDP) and in terms of investment opportunities since it provides an increase in the property value

    High-Dimensional Radial Symmetry of Copula Functions: Multiplier Bootstrap vs. Randomization

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    International audienceWe use a recently proposed fast test of copula radial symmetry based on multiplier bootstrap and obtain an equivalent randomization test. The literature shows the statistical superiority of the randomization approach in the bivariate case. We extend the comparison of statistical performance focusing on the high-dimensional regime in a simulation study. We document radial asymmetry in the joint distribution of the percentage changes of sectorial industrial production indices of the European Union

    Creditworthiness and buildings' energy efficiency in the Italian mortgage market

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    Energy efficiency represents one of the key planned actions aiming at reducing greenhouse emissions and the consumption of fossil fuel to mitigate the impact of climate change. In this paper, we investigate the relationship between energy efficiency and the borrower’s solvency risk in the Italian market. Specifically, we analyze a residential mortgage portfolio of four financial institutions which includes about 70,000 loans matched with the energy performance certificate of the associated buildings. Our findings show that there is a negative relationship between a building’s energy efficiency and the owner’s probability of default. Findings survive after we account for dwelling, household, mortgage, market control variables, and regional and year fixed effect. Additionally, a ROC analysis shows that there is an improvement in the estimation of the mortgage default probability when the energy efficiency characteristic is included as a risk predictor in the model

    Matrix-variate Smooth Transition Models for Temporal Networks

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    In many fields, network analysis is used to investigate complex relationships. The increased availability of temporal network data opens the way to the statistical analysis of the network topology and its dynamics. In addition network data are subject to measurement errors and random fluctuations. This calls for realistic time series models which account for relevant features of the data. In this chapter, we propose a new modeling and inference framework for studying matrix-valued panel data characterized by nonlinear dynamics and heavy tails. We assume a smooth transition model for the dynamics and a matrix-variate t distribution for the error term and show how the model can be used in temporal network analysis. Some properties of the model including the close-form expression for the predictor are given. We adopt a Bayesian approach to inference and design an efficient Markov chain Monte Carlo algorithm for approximating the posterior distribution. We apply the proposed model to a volatility network among European firms and an international oil production network and show its ability to account for structural changes. Our framework is motivated by temporal network data, nevertheless, it is general and can be of interest to all researchers interested in the analysis of matrix-variate time series

    Eccellenze cafoscarine nella storia del Dipartimento di Economia

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    In questo volume si ripercorre, attraverso i profili di alcuni tra i suoi rappresentanti celebri, la storia del Dipartimento di Economia dell’Università Ca’ Foscari Venezia, fin dalla sua istituzione come Regia Scuola Superiore di Commercio, il 6 agosto 1868. La raccolta comprende nove profili (alcuni più consistenti, altri più brevi ma non per questo meno significativi) di docenti che hanno insegnato presso l’ateneo veneziano: Francesco Ferrara (1810-1900), Primo Lanzoni (1862-1921), Gino Luzzatto (1878-1964), Silvio Trentin (1885-1944), Giulio La Volpe (1909-1996), Mario Volpato (1915-2000), Feliciano Benvenuti (1916-1999), Lycia Contursi-Lisi Gardani (1920-2004), Giulio Partesotti (1937-2004)
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