Forecasting of GDP Growth in the South Caucasian Countries Using Hybrid Ensemble Models Gaetano Perone, Manuel A. Zambrano-Monserrate Econometrics, 2025 This study aimed to forecast the gross domestic product (GDP) of the South Caucasian nations (Armenia, Azerbaijan, and Georgia) by scrutinizing the accuracy of various econometric methodologies. This topic is noteworthy considering the significant economic development exhibited by these countries in the context of recovery post COVID-19. The seasonal autoregressive integrated moving average (SARIMA), exponential smoothing state space (ETS) model, neural network autoregressive (NNAR) model, and trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), together with their feasible hybrid combinations, were employed. The empirical investigation utilized quarterly GDP data at market prices from 1Q-2010 to 2Q-2024. According to the results, the hybrid models significantly outperformed the corresponding single models, handling the linear and nonlinear components of the GDP time series more effectively. Rolling-window cross-validation showed that hybrid ETS-NNAR-TBATS for Armenia, hybrid ETS-NNAR-SARIMA for Azerbaijan, and hybrid ETS-SARIMA for Georgia were the best-performing models. The forecasts also suggest that Georgia is likely to record the strongest GDP growth over the projection horizon, followed by Armenia and Azerbaijan. These findings confirm that hybrid models constitute a reliable technique for forecasting GDP in the South Caucasian countries. This region is not only economically dynamic but also strategically important, with direct implications for policy and regional planning.
The relationship between labor market institutions and innovation in 177 European regions over the period 2000–2015 Gaetano Perone Structural Change and Economic Dynamics, 2024 The main goal of this paper is to investigate the relationship between labor market institutions (LMIs) and patents in 177 NUTS-1 and NUTS-2 European regions. Fixed effects models, ordinary least squares (OLS), the generalized method of moments estimation of the fixed effects (FE-GMM), multilevel modeling (MLM), and spatial models are employed. Patents are negatively correlated with EPL and union density and positively associated with wage bargaining coverage and centralization. As a result, a uniform wage that is higher than the competitive wage can enable the Schumpeterian creative destruction process, forcing firms to invest in innovation to remain in the market. Spatial analysis emphasizes that regional proximity promotes the flow of knowledge and increases the chance of innovation. Interactions also matter. Increased bargaining power and coordination, in particular, may outweigh the negative consequences of isolated EPL reforms. Thus, policies that strengthen wage-setting institutions are required in Europe to boost innovation.
The relationship between renewable energy production and CO2 emissions in 27 OECD countries: A panel cointegration and Granger non-causality approach Gaetano Perone Journal of Cleaner Production, 2024 Human-caused CO2 emissions are the primary cause of global warming. In this regard, determining the most effective approach for lowering CO2 emissions and the collateral risk of catastrophic natural disasters is crucial. This study examines the long-run relationship between disaggregated renewable energy production and carbon dioxide (CO2) emissions per capita for a panel of 27 OECD countries from 1965 to 2020. The panel-autoregressive distributed lag (ARDL) models of the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) were used to evaluate the relationship between CO2 emissions and energy production from biofuel, aggregated geothermal and biomass (GEOB), hydropower, nuclear, solar, and wind. As robustness checks, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and common correlated effects mean group (CCEMG) estimators were used. Then, using a generalized method of moment (GMM) framework for panel vector autoregression (PVAR), the Granger non-causality between CO2 emissions and renewable energy production was investigated. GEOB, hydropower, nuclear, solar, and wind were found to be negatively and significantly correlated with CO2 emissions. GEOB, hydropower, and solar were the most effective renewable resources in reducing CO2 emissions. Granger non-causality approach showed unidirectional causation from hydropower, solar, and wind to CO2 emissions, bidirectional causation between CO2, and biofuel and GEOB, and unidirectional causation from CO2 emissions to nuclear. The findings were consistent across different model specifications and suggested a faster transition to GEOB, hydropower, and solar energy in OECD countries in order to reduce CO2 emissions and enhance environmental sustainability.
Comments on the article "Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates" Gaetano Perone Osong Public Health and Research Perspectives, 2023 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
Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces Gaetano Perone Scientific Reports, 2022 This 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.
Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy Gaetano Perone European Journal of Health Economics, 2022 The 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.
Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries Gaetano Perone Econometrics, 2022 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.
Evaluating the impact of renewable energy, agriculture, and livestock on CO₂ and GDP in OECD countries using an extended STIRPAT framework G Perone Eurasian Economic Review, 1-54 , 2025 2025
Forecasting of GDP Growth in the South Caucasian Countries Using Hybrid Ensemble Models G Perone, MA Zambrano-Monserrate Econometrics 13 (3), 35 , 2025 2025 Citations: 1
Adaptive capacity to climate change: Asymmetric effects of energy aid and governance quality MA Zambrano-Monserrate, G Perone Climatic Change 178 (9), 167 , 2025 2025 Citations: 6
The impact of public healthcare system on COVID-19 mortality rate in selected European and South Caucasian countries G Perone Eurasian Economic Review, 1-46 , 2025 2025 Citations: 2
Prioritizing investments in public healthcare to address the COVID-19 outbreak: Evidence from Europe and the South Caucasus G Perone Health, Econometrics and Data Group (HEDG) Working Papers , 2024 2024
A Novel Hybrid Forecasting Model for Georgian GDP G Perone International School of Economics at Tbilisi State University (ISET-TSU), 1-15 , 2024 2024 Citations: 2
The relationship between renewable energy production and CO2 emissions in 27 OECD countries: A panel cointegration and Granger non-causality approach G Perone Journal of Cleaner Production 434, 139655 , 2024 2024 Citations: 122
The relationship between labor market institutions and innovation in 177 European regions over the period 2000–2015 G Perone Structural Change and Economic Dynamics 70, 128-149 , 2023 2023 Citations: 7
Comments on the article" Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates" G Perone Osong Public Health and Research Perspectives 14 (2), 146 , 2023 2023 Citations: 1
Prioritizing investments in public healthcare to address the COVID-19 outbreak G Perone Health, 9 , 2023 2023
Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces G Perone Scientific reports (Nature P.) 12 (1), 13317 , 2022 2022 Citations: 27
Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy G Perone The European Journal of Health Economics 23 (6), 917-940 , 2022 2022 Citations: 168
Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries G Perone Econometrics, MDPI 10 (2), 18 , 2022 2022 Citations: 43
The effect of labor market institutions and macroeconomic variables on aggregate unemployment in 1990–2019: Evidence from 22 European countries G Perone Industrial and Corporate Change 31 (2), 500-551 , 2022 2022 Citations: 13
Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy G Perone The European Journal of Health Economics, 1-24 , 2022 2022
The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors G Perone Science of the Total Environment 755, 142523 , 2021 2021 Citations: 138
Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy G Perone arXiv preprint arXiv:2010.11617 , 2020 2020 Citations: 13
The impact of agribusiness crimes on food prices: evidence from Italy G Perone Economia Politica-Journal of Analytical and Institutional Economics 37 (3 … , 2020 2020 Citations: 6
ARIMA forecasting of COVID-19 incidence in Italy, Russia, and the USA G Perone arXiv preprint arXiv:2006.01754 , 2020 2020 Citations: 18
An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy G Perone MedRxiv, 2020.04. 27.20081539 , 2020 2020 Citations: 124
MOST CITED SCHOLAR PUBLICATIONS
Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy G Perone The European Journal of Health Economics 23 (6), 917-940 , 2022 2022.0 Citations: 168
The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors G Perone Science of the Total Environment 755, 142523 , 2021 2021.0 Citations: 138
An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy G Perone MedRxiv, 2020.04. 27.20081539 , 2020 2020.0 Citations: 124
The relationship between renewable energy production and CO2 emissions in 27 OECD countries: A panel cointegration and Granger non-causality approach G Perone Journal of Cleaner Production 434, 139655 , 2024 2024.0 Citations: 122
Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries G Perone Econometrics, MDPI 10 (2), 18 , 2022 2022.0 Citations: 43
Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces G Perone Scientific reports (Nature P.) 12 (1), 13317 , 2022 2022.0 Citations: 27
I Costi Della Criminalità Organizzata Nel Settore Agroalimentare Italiano (The Costs of Organized Crime in the Italian Agro-Food Sector) G Perone Moneta e Credito 71 (281) , 2018 2018.0 Citations: 19
ARIMA forecasting of COVID-19 incidence in Italy, Russia, and the USA G Perone arXiv preprint arXiv:2006.01754 , 2020 2020.0 Citations: 18
The effect of labor market institutions and macroeconomic variables on aggregate unemployment in 1990–2019: Evidence from 22 European countries G Perone Industrial and Corporate Change 31 (2), 500-551 , 2022 2022.0 Citations: 13
Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy G Perone arXiv preprint arXiv:2010.11617 , 2020 2020.0 Citations: 13
When productivity is limited by the balance of payments. A reflection on the relationship between center and periphery in the european monetary union from the perspective of … S Lucarelli, G Perone Moneta e Credito 73 (292), 325-353 , 2020 2020.0 Citations: 9
Covid-19 and the MCO: An exit strategy for Malaysia C Ferlito, G Perone Brief IDEAS n. 20, Kuala Lumpur, Malaysia. , 2020 2020.0 Citations: 8
The relationship between labor market institutions and innovation in 177 European regions over the period 2000–2015 G Perone Structural Change and Economic Dynamics 70, 128-149 , 2023 2023.0 Citations: 7
Adaptive capacity to climate change: Asymmetric effects of energy aid and governance quality MA Zambrano-Monserrate, G Perone Climatic Change 178 (9), 167 , 2025 2025.0 Citations: 6
The impact of agribusiness crimes on food prices: evidence from Italy G Perone Economia Politica-Journal of Analytical and Institutional Economics 37 (3 … , 2020 2020.0 Citations: 6
An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy G Perone HEDG working paper n. 07/20, University of York, UK. , 2020 2020.0 Citations: 6
Economic growth and GHG reduction: a global issue G Perone Italian Review of Agricultural Economics 74 (1), 19-31 , 2019 2019.0 Citations: 6
Qualità degli Atenei e contesto socioeconomico. La sperequazione nell’allocazione delle risorse tra le università italiane R Realfonzo, G Perone Laurearsi vale. Le prospettive occupazionali dei laureati campani, 115-137 , 2016 2016.0 Citations: 6
La loggia P2 e il mondo finanziario italiano. Alcune evidenze empiriche basate sulla social network analysis S Lucarelli, G Perone Moneta e Credito 71 (284), 369-390 , 2018 2018.0 Citations: 5
Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy (HEDG-WP 20/18, University of York) G Perone Preprint. http://www. york. ac. uk/economics/postgrad/herc/hedg/wps , 0 Citations: 5
