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Projeto de investigação
Research in Economics and Mathematics
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Social expenditure cyclicality
Publication . Jalles, João Tovar; NOVA School of Business and Economics (NOVA SBE); Elsevier
This paper provides a novel dataset of time-varying measures of cyclicality in social spending for an unbalanced panel of forty-five developing economies from 1982 to 2012. We focus on four categories of government social expenditure: health, social protection, pensions, and education. We find that in developing countries social spending has been acyclical over time, with the exception of spending on pensions. However, sample averages hide marked heterogeneity across countries, with many individually showing procyclical behavior in different social spending categories. The use of time-varying measures of social spending cyclicality overcomes the major limitation of previous studies in assessing the drivers of fiscal cyclicality that rely solely on cross-country regressions and, therefore, cannot account for country-specific as well as global factors. Using weighted least squares regressions, we find that the degree of social spending (pro)cyclicality is negatively associated with financial deepening, the level of economic development, trade openness, government size, and political constraints on the executive.
Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis
Publication . Martins, Ana Alexandra; Vaz, Daniel C.; Silva, Tiago A. N.; Cardoso, Margarida; Carvalho, Alda; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DEMI - Departamento de Engenharia Mecânica e Industrial; MDPI - Multidisciplinary Digital Publishing Institute
In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/05069/2020