Matteo Pelagatti

A set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. See Maranzano and Pelagatti (2024) for details.

The increasing interest in sustainability within economics and finance has led to the widespread adoption of Environmental, Social, and Governance (ESG) metrics, expressed as ratings or in- dices, for assessing firms’ sustainable performance. However, inconsistencies among data providers stem not only from definitional differences but also from disagreements in measuring ESG factors. This paper proposes a novel approach by conversely focusing on ESG factors common to data providers. Through three empirical approaches—correlation analysis, principal component analysis, and panel data regressions—we aim to understand the structural components shaping ESG metrics, particularly in the Environmental Pillar. Our findings emphasize a limited number of indicators that act as common factors across three providers, primarily concerning the management of natural resources. Despite their shared thematic focus, depending on the provider, these indicators are approached with different perspectives—such as risk management, corporate impact management, and integration into corporate strategy. This analysis offers valuable insights for companies, financial institutions, practitioners, scholars, and policymakers, enabling more concise information for analyses and decision-making in their respective fields.

In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine learning literature. In particular, we implement Random Forests and support vector machines, which should automatically capture the relevant interactions among predictors. Given the large number of predictors, ARX models are also estimated using LASSO regularization, which improves predictions when regressors are many and selects the important variables. In addition to zonal intra-day prices, among the predictors we include also the official demand forecasts and wind generation expectations. Our results show that the prediction performance of the simple ARX model is mostly superior to those of machine learning models. The analysis of the relevance of exogenous variables, using variable importance measures, reveals that intra-day market information successfully contributes to the forecasting performance, although the impact differs among the estimated models.

Event Studies (ES) are statistical tools that assess whether a particular event of interest has caused changes in the level of one or more relevant time series. We are interested in ES applied to multivariate time series characterized by high spatial (cross-sectional) and temporal dependence. We pursue two goals. First, we propose to extend the existing taxonomy on ES, mainly deriving from the financial field, by generalizing the underlying statistical concepts and then adapting them to the time series analysis of airborne pollutant concentrations. Second, we address the spatial cross-sectional dependence by adopting a twofold adjustment. Initially, we use a linear mixed spatio-temporal regression model (HDGM) to estimate the relationship between the response variable and a set of exogenous factors, while accounting for the spatio-temporal dynamics of the observations. Later, we apply a set of sixteen ES test statistics, both parametric and nonparametric, some of which directly adjusted for cross-sectional dependence. We apply ES to evaluate the impact on NO2 concentrations generated by the lockdown restrictions adopted in the Lombardy region (Italy) during the COVID-19 pandemic in 2020. The HDGM model distinctly reveals the level shift caused by the event of interest, while reducing the volatility and isolating the spatial dependence of the data. Moreover, all the test statistics unanimously suggest that the lockdown restrictions generated significant reductions in the average NO2 concentrations.