ABSTRACT
This study explores the effect of green bonds, oil prices, and the coronavirus disease 2019 (COVID-19) pandemic on industrial carbon dioxide (CO2) emissions. In this context, this study examines the United States of America (USA), which is the biggest economy in the world, uses weekly data between March 6, 2020 and September 30, 2022, and applies a novel wavelet local multiple correlation (WLMC) approach under time-varying and frequency-varying perspective. The novel empirical findings shows that (i) there is a strong negative (positive) co-movement between industrial CO2 emissions and green bonds in the short-run (long-run);(ii) there is a strong positive (negative) co-movement between industrial CO2 emissions and oil price in the medium-run (long-run);(iii) there is a strong negative (positive) co-movement between industrial CO2 emissions and the COVID-19 pandemic in the medium-run (long-run);(iv) the oil price is the dominant factor, whereas there are changing effect of the variables on each other at different times and frequencies;and (vi) overall, there are long-run asymmetric and dynamic correlations between industrial CO2 emissions and variables. Hence, the empirical results highlight the asymmetric, time-varying, and frequency-varying effects of green bonds, oil prices, and the COVID-19 pandemic on industrial CO2 emissions by presenting fresh and novel evidence. Moreover, the study proposes policy implications for the USA government. © The Author(s) 2023.
ABSTRACT
This study examines the impacts of financial and macroeconomic factors on financial stability in emerging countries by focusing on Turkey's banking sector. In this con-text, financial stability is represented by nonperforming loans (NPLs). Four financial and three macroeconomic indicators as well as the Covid-19 pandemic are included as explanatory variables. Quarterly data from 2005 Q1 to 2020 Q3 are analyzed by using the residual augmented least squares unit root test and generalized method-of-moments. The empirical results show the following: credit volume, which is a financial indicator, has the greatest effect on NPLs;risk-weighted assets, unemployment rate, foreign exchange rate and economic growth all have a statistically significant impact on NPLs;the Covid-19 pandemic has had an increasing impact on NPLs;inflation and interest rates have a positive coefficient, as expected, although they are not statistically significant. These results highlight the importance of financial factors (ie, credit volume and risk-weighted assets) over macroeconomic factors in terms of NPLs. Based on the empirical results of the study, we suggest Turkish policy makers focus primarily on financial variables (ie, credit growth and risk-weighted assets) as well as considering the effects of other factors. © Infopro Digital Limited 2023.
ABSTRACT
This study aimed to examine physical education teachers' perceptions of work alienation in Turkey according to different variables (including gender, marital status, school level, availability of a gym in the school, age, and years of service) during the COVID-19 pandemic, which affected sustainability in education on a global scale. The study employed the survey method and research data were collected from 442 volunteer physical education teachers working in different provinces of Turkey through the "Physical Education Teachers' Alienation to Work Scale”. The results showed that physical education teachers had low levels of alienation in their work. The scale's subdimensions ‘occupational isolation' and ‘powerlessness' indicated higher levels of work alienation compared to other subdimensions. Among teachers who had completed their graduate education, the level of work alienation was higher in the subdimensions ‘powerlessness' and ‘occupational alienation'. Based on a comparison with prior research on sustainability in education, the COVID-19 pandemic could be said to have no significant impact on physical education teachers' levels of work alienation. The cause of work alienation among physical education teachers was structural issues rather than specific time-bound events such as the COVID-19 pandemic. © 2023 by the authors.
ABSTRACT
COVID-19 virus;has dragged the world into an epidemic that has infected more than 413 million people and caused the death of nearly 6 million people. Although biomedical tests provide the diagnosis of COVID-19 with high accuracy in the diagnosis of the disease, it increases the risk of infection due to the fact that it is a method that requires contact. Machine learning models have been proposed as an alternative to biomedical testing. Cough has been identified by the World Health Organization as one of the symptoms of COVID-19 disease. In this study, the success performance of the positive case situation with machine learning was examined using the COUGHVID dataset with cough voice recordings. In order to increase the performance of the model, MFCC, Δ-MFCC and Mel Coefficients attributes were obtained after preprocessing the sound recordings. In the ensemble learning model, features were used as independent variables and a value of 0.65 AUC-ROC was reached. In addition to these performance-enhancing changes, since the acoustic properties of male and female cough sounds are different, the training of persons was carried out separately from each other, and AUC-ROC values of 0.70 for females and 0.68 for males were obtained. Trimming the silent regions at the beginning and end of the recordings, using the ensemble learning model, and grouping based on gender provided better results for this study compared to previous studies. © 2022 IEEE.
ABSTRACT
The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods;(ii) high-frequency data increases the performance of estimation models;(iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data;(iv) the pandemic causes an adverse effect on the performance of estimation models;(v) energy-related variables are more important than other variables although all are significant;(vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed. © 2022 Elsevier Ltd
ABSTRACT
Purpose: By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks. Design/methodology/approach: A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness. Findings: Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables;the pandemic and rent have the highest effect on the indices;The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles;the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles;the results for RPPI, NRPPI and ORPPI are consistent and robust. Research limitations/implications: The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study. Practical implications: The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables. Social implications: Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets. Originality/value: The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens. © 2021, Emerald Publishing Limited.
ABSTRACT
Background: To investigate the prevalence and clinical and laboratory characteristics of the cases with pulmonary embolism (PE) in the pace of coronavirus disease-2019 (COVID-19). Materials and methods: COVID-19 patients' records were retrospectively scanned from the hospital's automation system and recorded on patients' files. Results: Of 1452 COVID-19 patients, 17 (1.2%) were diagnosed with PE. Compared cases with PE with controls, it was seen that mean age was higher (p=0.036), male gender was prominent (p=0.016), patients presented with dyspnea symptoms further (p<0.001), while O2 saturation measured at room air on admission was lower (p=0.002). In PE patients, glucose (p=0.007), D-dimer (p<0.001), C-reactive protein (p<0.001) and ferritin levels (p=0.002) were higher than controls. In Receiver-Operator Characteristics analysis, the cut-off value of D-dimer in predicting PE was found to be 4211 ng/mL (p<0.001). COVID-19 patients were diagnosed with PE median five (min:max=0:36) days after hospitalization. Additionally, PE patients were found to have longer hospitalization time (p<0.001), the requirement for caring in the intensive care unit (p<0.001), and intubation (p=0.001), and non-invasive mechanical ventilation (p<0.001) in more patients, compared to controls. Mortality rates were similar in both groups, with three and 106 deaths in PE and control groups, respectively. Lower-extremity Doppler ultrasonography was performed in 196 patients, and thrombi were detected in the femoral vein in four patients, also presenting with PE. Conclusions: Even if there is no embolism without any obvious clinic of PE in all cases with COVID-19, such cases should be screened for PE in the presence of significant D-dimer elevation. © 2021 A. CARBONE Editore. All rights reserved.
ABSTRACT
With the emergence and spreading of COVID-19 pandemic all over the world, the uncertainty has been increasing for countries. Depending on this condition, especially emerging countries have been affected negatively by foreign portfolio investment outflows from stock exchanges, and main stock exchange indices have been collapsed. The study examines the causes of the main stock exchange index changes in Turkey in the COVID-19 period. In this context, 14 variables (3 global, 6 country-level, 5 market-level) are analyzed by employing random forest and support vector machine algorithms and using daily data between 01.02.2020 and 05.15.2020, which includes the pre-pandemic and the pandemic periods. The findings prove that (i) the most important variables are the retention amount of foreign investors in the equity market, credit default swap spreads, government bonds interest rates, Morgan Stanley Capital International (MSCI) emerging markets index, and volatility index in the pre-pandemic period;(ii) the importance of variables changes as MSCI emerging markets index, the volatility index, retention amount of foreign investors in the equity market, amount of securities held by the Central Bank of Republic of Turkey (CBRT), equity market traded value in the pandemic period;(iii) support vector machine has superior estimation accuracy concerning random forest algorithms in both pre-pandemic and pandemic period.