Gaetano Perone
@unipi.it
Post-doctoral Research Fellow
University of Pisa
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Gaetano Perone
Korea Disease Control and Prevention Agency
To the Editor: I read the recently published article by Kim et al. [1]. On page 424 [1], the authors state, referring to my paper [2], that “other research using time-series cross-sectional data appears to have underestimated the impact of autocorrelation and heteroscedasticity”. However, this statement is incorrect and unfounded for 2 reasons. First, I used cross-sectional data rather than panel data, so there was no time component. The corollary is that residuals cannot be serially correlated. It makes no sense to consider autocorrelation in this case. Second, as shown in Section 5.1 of Perone [2], I safely considered heteroscedasticity in my paper: “Furthermore, since Breusch and Pagan (1979) and Shapiro and Wilk (1965) tests allowed to accept the null hypothesis of homoscedasticity and normality of residuals, models seemed well specified. However, due to the small sample, I preferred to adopt a conservative approach, by applying the HC2 correction proposed by MacKinnon and White (1985)” [3−5]. As a result, autocorrelation and heteroscedasticity issues have no bearing on the results of my paper. Notes
Gaetano Perone
Springer Science and Business Media LLC
AbstractThis paper investigates the air quality in 107 Italian provinces in the period 2014–2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants—nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10)—were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO2, PM2.5, PM10, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO2, O3, PM2.5, PM10, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.
Gaetano Perone
Springer Science and Business Media LLC
AbstractThe coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic’s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020–October 13, 2020 were extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities’ decision-making, especially in the short-term.
Gaetano Perone
MDPI AG
The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 46 out 48 metrics (in forecasting future values), i.e., on 95.8% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.
Gaetano Perone
Oxford University Press (OUP)
Abstract This paper investigates the long-run effect of a wide set of labor market institutions (LMIs) and macroeconomic variables on aggregate unemployment for a panel of 22 European countries over the period 1990–2019. First-difference feasible generalized least squares model, Prais-Winsten regression with panel-corrected standard errors, two-step generalized method of moments estimation of the fixed effects, and fixed-effects regression with Driscoll and Kraay standard errors are estimated. The results suggest that employment protection legislation, wage bargaining coordination and centralization, minimum wage, and immigration inflows are significantly and negatively associated with the aggregate unemployment rate. Conversely, union density, product market regulation (PMR), and tax wedge have a positive and significant correlation with unemployment rate. The impact of corporate tax rate and government size is mostly positive. Moreover, the interaction between LMIs does matter and may sometimes change the interpretation of some reforms taken in isolation. Stronger wage-setting institutions may offset the negative impact of PMR and the tax wedge. Macroeconomic variables are generally consistent with the major literature and do not change LMIs interpretation. Among macroeconomic factors, capital accumulation plays the most important role in reducing the unemployment rate. Finally, my findings suggest the implementation of economic policies consistent with Keynesian theory and all those economists—such as Solow (1990)—who look at the labor market as a social institution.
Gaetano Perone
Springer Science and Business Media LLC
Gaetano Perone
The environmental sustainability is probably one of the most controversial topics of national policy agendas. The needs to combine economic growth and well-being, have forced governments to introduce tools for reducing CO2 emission and avoiding climate change. This paper aims to assess the effectiveness of these measures in the 1990-2014 period for a sample of 188 countries; and to analyze the determinants of CO2 in the 2000-2014 period for a sample of 175 countries. The results suggest that i) richest countries have a GDP elasticity of CO2 greater than that of poorest countries; and ii) GDP, energy consumption, urbanization, agricultural development, tourism and depletion of natural resources are directly correlated to CO2, while forest area, alternative energy, trade openness and FDI inflows are inversely related to CO2.