5,245 research outputs found
Turkish Stock Market from Pandemic to Russian Invasion, Evidence from Developed Machine Learning Algorithm
No full textIn recent time, the two significant events; Coronavirus epidemic and Russian invasion are effecting all over the world in various aspects; healthily, economically, environmentally, and socially, etc. The first event has brought uncertainties to the economic situation in most countries based on the epidemic transmission. In addition to that, on 24th February 2022 the Russian invasion of Ukraine affected negatively almost all stock markets all over the world, but the effects are heterogeneous across countries according to their economic-political relationship or neighbourhood, etc. Due to that, the stock market price in Turkey has been affected dramatically over that period. This empirical study is the first attempts to explore the impact of Coronavirus epidemic and Russian invasion on the stock market index XU100 in Turkey by applying the developed statistical method namely elastic-net regression based on empirical mode decomposition which can precisely tackle the nonstationary and nonlinearity data. Then we performed the robustness check by applying a nonlinear techniques Markov switching regression. The data are collected from the beginning of the epidemic in Turkey from March 11, 2020 until May 31, 2022. The finding reveals that there is significant effect of the Coronavirus spreading on the Turkish stock market index, particularly during the first wave. Then after the Russian Invasion the XU100 index is effected more negatively. As the credit default swap and TL reference interest rate have a negative impact but the foreigner exchange rate has a positive significant impact on the XU100 index, and it varies according to the period of short term and long term. Moreover, the results obtained by using the robustness check shows a robust and consistent finding. In conclusion, understanding the impact of Coronavirus pandemic and Russian invasion on the Turkish stock market can provide important implications for investors, financial sectors, and policymakers
Association between coronavirus cases and seasonal climatic variables in Mediterranean European Region, evidence by panel data regression
No full textThe coronavirus pandemic is one of the most fast-spreading diseases in the history, and the transmission of this virus has crossed rapidly over the whole world. In this study, we intend to detect the effect of temperature, precipitation, and wind speed on the Coronavirus infected cases throughout climate seasons for the whole year of epidemic starting from February 20, 2020 to February 19, 2021 with considering data patterns of each season separately; winter, spring, summer, autumn, in Mediterranean European regions, whereas those are located at the similar temperature zone in southern Europe. We apply the panel data approach by considering the developed robust estimation of clustered standard error which leads to achieving high forecasting accuracy. The main finding supports that temperature and wind speed have significant influence in reducing the Coronavirus cases at the beginning of this epidemic particularly in the first-winter, spring, and early summer, but they have very weak effects in the autumn and second-winter. Therefore, it is important to take into account the changes throughout seasons, and to consider other indirect factors which influence the virus transmission. This finding could lead to significant contributions to policymakers in European Union and European Commission Environment to limit the Coronavirus transmissions. As the Mediterranean region becomes more crowded for tourism purposes particularly in the summer season
A validation forecast using robust estimators into environmental application
No full textInvestigating the effect of energy consumption and economic growth on carbon dioxide emissions has received much effort due to the global environmental issues. Multiple methods are used to explain that relationship, but findings are conflicting, that might due to inaccurate chosen statistical methods and the frequent presence of outliers in the data. The main objective of this study is to shed light on obtaining the best model in detecting that relationship using various robust estimators (M, Median, S and MM-estimator) against OLS in the presence of different types of outliers in the panel data, then, models are evaluated by using train-test forecasting approach. The panel data include 29 countries, divided into two groups based on the economic level, 17 developed countries versus 12 developing countries from 1960 to 2008. The main findings support that the robust estimators have better properties than the OLS estimator when the dataset has outliers. The M-estimator is the best robust estimator which could fit the data in the presence of different types of outliers. The energy consumption and economic growth have negative and positive relationship with CO2 emissions, respectively. Moreover, developing countries affect CO2 emissions more than the developed countries. In conclusion, energy consumption contributes to higher environmental degradation particularly in CO2 emissions, thus it is recommended to policy makers to consider it in their policies for a better future
Quantile Regression to Tackle the Heterogeneity on the Relationship Between Economic Growth, Energy Consumption, and CO2 Emissions
No full textHeterogeneity in the data is a common issue arising in research. When data are heterogeneous, equal variation in the data to set up a model for the studied phenomena cannot be assumed. Ordinary least square regression does not consider the unequal variation which may provide inefficient estimation of the relationship between variables. On the contrary, quantile regression could efficiently tackle this problem by detecting the relationship between variables at different levels, and could be useful especially in applications where extreme values are important to consider, such as in environmental studies, where upper quantiles of pollution levels are critical from a public health perspective. The main purpose of this study is to model the relationship between CO2, economic growth, and energy consumption by considering the heterogeneity problem for developed and developing countries and applying the quantile regression at different percentile values (0.05, 0.25, 0.50, 0.75, and 0.95) on panel data. The panel data consists of 29 countries from two different economic development groups: 17 developed versus 12 developing countries—over the period 1960–2008. Quantile regression (QR) results are then compared with those of the OLS model, resulting similar for developed and developing countries. In both cases, countries having lower GDP release less CO2 emissions
Regional economic convergence in federation contexts: a comparative analysis of Brazil and the European Union
No full textThis article examines regional economic convergence in two federal contexts: Brazil and the European Union. Despite many differences, in the last decades these two economies have shown irregular economic growth and been facing economic inequalities, launching public policies to reduce them. We analyse the tendencies of regional convergence within these economies between 2002 and 2019, focusing on σ-convergence, absolute convergence, convergence clubs and the transitional behaviour of club members. The results show that in both cases convergence occurs, but at a slow rhythm, especially in the European Union
Air demand forecasting for passengers and freight in Italy: A comparison of two statistical models
No full textAir transport forecasting has received significant attention in the literature. Furthermore, economic growth and population are significantly associated with the aviation industry. Moreover, the time series of air passenger and freight demand usually exhibit a complex behaviour with high volatility and irregularity, particularly when considering the economic factors associated with freight demand. In this research, we implemented two different statistical methods, namely the SARIMAX model and the structural time series approach, to fit and forecast both the air passenger and freight demand, considering economic variables and population as regressors. Both methods can deal with seasonality and trend; interestingly, the structural time series model can also estimate the cycle by decomposing the time series using the Kalman filter method. We applied the two methods to monthly data obtained from the Italian national website Assaeroporti for the period from January 2000 to December 2023. We computed predictions for the passenger and freight demand up to 2035, with a monthly and yearly resolution. For this aim, it was necessary to implement separate time series models for the economic regressors and population to plug in corresponding forecasts in the demand models. The results could be particularly useful for optimizing air traffic infrastructure and guiding strategic investment, particularly in the planning and adoption of sustainable aviation technologies (e.g., electric and hybrid-electric systems, new sustainable fuels)
The effects of economic growth and fossil fuel consumption to climate change: Evidence from Mediterranean Europe by robust estimators
No full textOptimizing Modelling Accuracy Using Variational Mode Decomposition and Elastic Net Regression: Evidence in Stock Market Prediction
No full textAccurate modelling of complex, nonlinear and nonstationary datasets remains a critical challenge in predictive analytics. This study introduces a novel variational mode decomposition-elastic net regression (VMD-Enet) framework that combines VMD with ENet to enhance prediction accuracy and interpretability. VMD first decomposes signals into intrinsic mode functions (IMFs), effectively denoising data and improving feature representation. ENet is then applied to select the most significant predictors while managing multicollinearity. The proposed approaches are evaluated using numerical simulations and real stock market data. The proposed VMD-ENet model demonstrated superior performance over the other methods. In the case of the stock market experimental analysis, VMD-ENet achieved the lowest errors, with RSS = 88.90, RMSE = 0.837, MAE = 0.668,
and WQE = 0.0006. Compared to other regularization approaches, VMD-ENet significantly identifies key predictors without arbitrarily discarding correlated variables, ensuring model stability. These findings highlight the framework's robustness, interpretability and predictive superiority, making it a promising tool for financial market analysis and broader applications in complex data modelling
Improving accuracy models using elastic net regression approach based on empirical mode decomposition
No full textIn this study, an elastic net (EN) regression model based on the empirical mode decomposition (EMD) algorithm is used in two applications, namely, numerical experiment and actual time series data. EMD is used to analyze a nonstationary and nonlinear signal dataset, which includes a set of orthogonal intrinsic mode functions (IMFs) and residual components. EN regression is used to select the most significant predictor variables influencing response variables and can address the multicollinearity problem between predictor variables. The main objective of this study is to apply the proposed method, EMD-EN, by using two variables for selecting important orthogonal IMFs and the residual components of predictor variables with significant effects on response variables. Moreover, this study uses the EMD-EN method in two different applications involving nonstationary and nonlinear problems. Results show that the proposed method outperforms other competitive methods in the numerical experiment and applications
Regional economic convergence in federation contexts: a comparative analysis of Brazil and the European Union
No full textThis article examines regional economic convergence in two federal contexts: Brazil and the European Union. Despite many differences, in the last decades these two economies have shown irregular economic growth and been facing economic inequalities, launching public policies to reduce them. We analyse the tendencies of regional convergence within these economies between 2002 and 2019, focusing on !-convergence, absolute convergence, convergence clubs and the transitional behaviour of club members. The results show that in both cases convergence occurs, but at a slow rhythm, especially in the European Union
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