Aquaculture Production in the Midst of GHG Emissions in South Africa

The study ascertained the relationship between aquaculture production and greenhouse gas (GHG) emissions in South Africa. The study used the Autoregressive Distributed Lag—Error Correction Model (ARDL-VECM) with time series data between 1990 and 2020. The results showed that the mean annual aquaculture production, GHG emissions, and Gross Domestic Product (GDP) in the period were 5200 tonnes, 412 tonnes, and US$447 billion, respectively. There was a long-run relationship between GHG emissions and GDP. In the short run, GHG emissions had a positive relationship with GDP and a negative relationship with beef production. Furthermore, there was a bi-directional relationship between aquaculture production and GHG emissions. In addition, beef production and GDP had a bi-directional relationship. Beef production also had a positive relationship with aquaculture production. The study concludes that aquaculture production is affected and tends to affect GHG emissions. Aquaculture legislation should consider GHG emissions in South Africa and promote sustainable production techniques.

RECOVERY PERIOD OF AIR TRANSPORTATION: A FORECAST WITH VECTOR ERROR CORRECTION MODEL

Air transport is the primary module of civil aviation and because of its nature, air transport has been simultaneously affected by Pandemics and crises. The influence of COVID-19 was more devastating than the other Pandemics and crises due to its global effect. This effect has continued a long period that still this effect exists now with a slight trend. The aim of this study is to analyse the selected variables that shows the past and future trend of air transportation related to operational and financial status. These variables are the primary ones that can define the countries' general status in air transport. The forecasting results are examined by 9-months forecasting with Vector Error Correction Model. It is forecasted that slightly decreasing trend will proceed in the following 9-months for passenger transportation due to fall and winter seasons. It is forecasted that slightly upward trend will proceed in the following 3-months and slightly decreased in the other 6-months for cargo transportation due to potential economic crisis in 2023. The originality of this paper is the first research related to analyse passenger and freight transportation together with the operational and financial parameters that defined in the sample of data and methodology sections. © 2023 Journal of Cellular & Molecular Anesthesia. All rights reserved.

Forecasting Recovery Period of the Airfreight Transportation from Covid-19 Pandemic by using Time Series Modelling

COVID-19 has a dramatically negative effect globally, so all transportation modes also airfreight have been affected negatively. This study aims to forecast the airfreight load factor by applying time series to the selected variables. After providing general information about COVID-19, the forecasting results apply to the time series modeling finding the getting back time into the recovery period. It analyzes between January 2016-May 2021 with available tonne-kilometer, revenue tonne-kilometer, load factor, gross domestic product, domestic and international freight. The findings show that the cargo load factor is affected by domestic transportation in the long-term and international transport in the short-term periods. So, airfreight is firstly affected by international transport due to its global position. The forecast results show that the recovery period started in February 2021 and will continue with a robust growth trend in July 2021 due to the changing airlines' focus on freight transportation. After the completion of vaccination, primarily related to passenger transportation, airfreight transportation also benefits from this growth trend with the configuration change of aircraft'. This paper's contribution shows the necessity to minimize the economic damage by using passenger aircraft for freight transport to increase the speed of the recovery period in terms of GDP.

Impact of COVID-19 on the NSE Nifty and the gross domestic product: Evidence from India

The purpose of this research is to look into the relationship between the NSF. Nifty and the Gross Domestic Product. During the Covid-19 epidemic, changes in the relationship between the NSF. Nifty and the GDP are investigated. For the years 2000-2001 to 2020-2021 and Q4:2018-19 to Q1:2021-22 of Gross Domestic Product (GDP) and NSF. Nifty, both annual and quarterly data are used. Unit root tests, Johansen cointegration tests, the Vector error correction model (VECM), the Wald test, the Granger Causality test, and the Karl-Pearson coefficient of correlation were all utilized. The Johansen cointegration test indicates that the NSE Nifty and GDP have a long-run relationship. Similarly, the results of the vector error correction model demonstrate that the NSE Nifty Index has a positive impact on GDP. According to the results of the Granger causality test, the NSF. Nifty is the most important indicator of GDP during the Covid-19 period. From the time of the Pre-Covid-19 pandemic until the time of the Covid-period, the strength of the link has grown stronger.

Inter-regional dependence of J-REIT stock prices: A heteroscedasticity-robust time series approach

This paper investigates the dynamic interdependence between the stock returns of geographically non-overlapping Japanese real estate investment trusts (J-REITs), where the property type and a market return are controlled. We take a multivariate time series approach, allowing for conditional heteroscedasticity of unknown form. We find significant impacts of central J-REITs on local J-REITs in conditional mean, a potential signal of arbitrage opportunities. After the COVID-19 crisis, the central-to-local impacts have become stronger for all property types considered: office, residential, and hotel. This empirical result is consistent with the consensus that portfolio diversification is harder to achieve during a period of turmoil. © 2022 Elsevier Inc.

Environmental effect of the Coronavirus-19 determinants and lockdown on carbon emissions

BACKGROUND AND OBJECTIVES: Coronavirus-19 has affected carbon emissions, which was declared as a pandemic by World Health Organization. Unprecedented environmental effects are being caused by Bangladesh's strict lockdown policies, which were implemented to stop the spread of Coronavirus-19. However, it is still unclear how the temporary halting and restart of industrial and commercial activities will affect the environment. In this study, it has been identified how Coronavirus-19 determinants like lockdown, daily confirmed cases, and daily confirmed deaths affect greenhouse gases. METHODS: From March 18, 2020 to February 4, 2022 the data series is used for Bangladesh. To ensure that the data series were stationary, the Augmented Dickey-Fuller and Phillips-Perron tests were utilized. Johansen co-integration test was utilized to determine co-integration among variables. The Granger causality test was utilized to identify directional causes and effects between Coronavirus-19 determinants and carbon emissions and the Vector Error Correction Model was employed to determine short-run and long run connections. FINDINGS: The study finds a bidirectional relationship between lockdown, carbon emissions and daily confirmed deaths, while a unidirectional association exists among Coronavirus-19 confirmed cases according to the Vector Error Correction Model. The Granger causality test also established the relationship between variables, except for daily confirmed cases. The pandemic's onset and subsequent lockdown resulted in decreased carbon dioxide emissions. The short-run link of carbon dioxide emissions with newly confirmed cases was corroborated by the directional relationship of variables, whereas there was a long-term and short-term association between confirmed deaths and lockdown. CONCLUSION: The reduction in carbon emissions during the pandemic will not be long-lasting because it is anticipated that global economic activity will gradually return to the preCoronavirus-19 state. The directional and relational nature of lockdown offers the potential to connect carbon dioxide emissions to regular lives. During a lockdown, there is a connection between the atmosphere's changes and how natural organisms behave. Importantly, there is a room for investigation into how communities of organisms and the atmosphere would function without humans. The essential point is to stress that during the lockdown, the ecosystem is self-healing. Environmental activists and business people will find this study useful in developing future sustainable improvement strategies.

Wagner’s Law vs. Keynesian Hypothesis: Dynamic Impacts

This study analyzes the dynamics between public expenditure and economic growth in Peru for 1980Q1–2021Q4. We used quarterly time series of real GDP, public consumption expenditure, public expenditure, and the share of public expenditure to output. The variables were transformed into natural logarithms, wherein only the logarithm of public expenditure to output ratio is stationary and the others are non-stationary I1. The study of stationary time series assesses whether Wagner’s law, the Keynesian hypothesis, the feedback hypothesis, or the neutrality hypothesis is valid for the Peruvian case according to Granger causality. We found cointegration between real GDP and public expenditure, and public consumption expenditure and real GDP. Estimating error correction and autoregressive distributed lag models, we concluded that Wagner’s law and the Keynesian hypothesis are valid in the Peruvian case, expressed as dynamic processes that allow us to obtain short-run and long-run impacts, permitting the mutual sustainability of economic growth and public expenditure.

Spillover effect among independent carbon markets: evidence from China’s carbon markets

Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Does COVID-19 affect GDP? A relationship between GDP and unemployment rate

This study aims to examine the long-term relationship between the unemployment rate and the growth of domestic product (GDP) in Malaysia, thereby revealing unemployment's impact on GDP. In this COVID-19 pandemic situation, numerous people have lost their jobs. That indirectly increases the unemployment rate which later has a variety of negative consequences on the government, society, and individuals. The Malaysian government has taken a big step in announcing the Movement Control Order (MCO) to slow down the spread of infections. Such decisions have affected the unemployment rate, as some businesses have to reduce their employees and some high-risk companies temporarily closed to stop the spreading of COVID cases. The cointegration test is employed to identify the relationship between the unemployment rate and GDP and then validate it by analyzing the error. Quarterly unemployment rate and GDP data were obtained from the Department of Statistics Malaysia (DOSM) website from the first quarter of 2010 to the fourth quarter of 2020. The study found that the variables were stationary at first differencing and long-run relationships existed among them. According to the empirical findings in this study, long-run and short-run unemployment rates have a high influence on the GDP rate. However, the result contradicted one work in literature that claimed a negative association between GDP and unemployment for the past fifty years. This could have occurred as a result of the worldwide COVID-19 pandemic. © 2022 The Authors.

Forecasting Recovery Period of the Airfreight Transportation from Covid-19 Pandemic by using Time Series Modelling

COVID-19 has a dramatically negative effect globally, so all transportation modes also airfreight have been affected negatively. This study aims to forecast the airfreight load factor by applying time series to the selected variables. After providing general information about COVID-19, the forecasting results apply to the time series modeling finding the getting back time into the recovery period. It analyzes between January 2016-May 2021 with available tonne-kilometer, revenue tonne-kilometer, load factor, gross domestic product, domestic and international freight. The findings show that the cargo load factor is affected by domestic transportation in the long-term and international transport in the short-term periods. So, airfreight is firstly affected by international transport due to its global position. The forecast results show that the recovery period started in February 2021 and will continue with a robust growth trend in July 2021 due to the changing airlines’ focus on freight transportation. After the completion of vaccination, primarily related to passenger transportation, airfreight transportation also benefits from this growth trend with the configuration change of aircraft’. This paper’s contribution shows the necessity to minimize the economic damage by using passenger aircraft for freight transport to increase the speed of the recovery period in terms of GDP. © 2022, Bundesvereinigung Logistik (BVL). All rights reserved.

The impact of the Covid-19 crisis on the liquidity of cryptocurrencies

We tracked the impact of the Covid-19 crisis on the liquidity of10 crypto currencies for the period from July 31, 2019 (before the crisis) to December 31, 2020 (in the Covid -19 era). We applied the vector error correction model to each crypto currency. The results show that in the short term, the COVID-19 crisis has no influence on the liquidity of cryptocurrencies except for Cardano. Similarly, in the long term, it has no impact on the liquidity of cryptocurrencies with the exception of Binance coin, Tezos and Cardano. The assumption of having a common liquidity factor implies that, in a shock of liquidity, the entire market will be affected. The cryptocurrency market, however, has proven to be different. © 2022 IEEE.

A state-space approach to time-varying reduced-rank regression

We propose a new approach to reduced-rank regression that allows for time-variation in the regression coefficients. The Kalman filter based estimation allows for usage of standard methods and easy implementation of our procedure. The EM-algorithm ensures convergence to a local maximum of the likelihood. Our estimation approach in time-varying reduced-rank regression performs well in simulations, with amplified competitive advantage in time series that experience large structural changes. We illustrate the performance of our approach with a simulation study and two applications to stock index and Covid-19 case data. [ FROM AUTHOR] Copyright of Econometric Reviews is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Contagion Effects in ASEAN-5 Exchange Rates During the Covid-19 Pandemic

The aim of this study is to examine the pure and fundamental-based contagion effects in ASEAN-5 exchange rates during Covid-19 period using daily exchange rates from June 2019 to December 2020. We adopt VECM within the structural VAR framework and higher time-frequency wavelet analysis. The VECM findings show that ASEAN-5 exchange rates are cointegrated during this pandemic and should there be any disequilibrium, daily rate of adjustments in the Indonesian rupiah, Malaysia ringgit and Singapore dollar are 6.58%, 1.47% and 2.45% respectively. The wavelet power spectrum implies that Indonesia, Malaysia and Singapore experience prolonged high degree of exchange rates volatility, Thailand experiences mild volatility in the short run and high volatility in the long run and only Philippines experiences mild volatility in the short run and no heightened long run volatility. The wavelet coherence shows Indonesian rupiah reacts first to the Covid-19 shock leading to fundamental- based contagion to Malaysia and Thailand, and temporary pure contagion based on sentimental to Philippines and Singapore. Only the Philippine peso that insulates itself from the long run shocks. These findings are important as it gives insights into the nature of contagion among ASEAN-5 exchange rates due to global shock of Covid-19 and the need for timely intervention to prevent the short run contagion turning into the long run.

Impact of COVID-19 on volatility of BSE Sensex stock index

The present study investigates the impact of COVID-19 on the volatility of BSE Sensex stock index. The weekly data on COVID-19 fatality cases, an independent variable, in India from 1 March 2020 to 27 December 2020 has been taken from the official website of the World Health Organization. The weekly data on a dependent variable (Sensex) and control variables (crude oil, Bitcoin, Ethereum, Litecoin) have also been considered for the period under study. The GARCH(1, 1) model has been used to extract the volatility series of the variables that are considered in the investigation, and vector error correction model (VECM) is also applied. Further, robust tests like ADF, variance decomposition test, impulse response test have been performed to check the validity of the results. The findings suggest the significant negative effect of COVID-19 fatality cases on BSE Sensex stock index during the specified study period. This negative coefficient of COVID-19 fatality cases in India reflects the increasing volatility of the BSE Sensex stock index. © 2022 Inderscience Enterprises Ltd.

Reconnoitering Price Discovery and Market Efficiency Process Among Indian HRITHIK Stocks Using VAR Causality and VECM Tests

The purpose of this paper was to ascertain the impact of futures prices on market efficiency and price discovery in India in HRITHIK stocks from 2017 – 2020. The paper investigated the impact of futures prices on market efficiency and price discovery in India in HRITHIK stocks from 2017 – 2020. The current study comprised the daily near-month futures and daily spot closing prices of the HRITHIK stocks from January 1, 2017 – December 31, 2020, including the COVID-19 pandemic period. The paper used the vector autoregression (VAR) Engel Granger causality test to test the short-run equilibrium between spot and futures prices and the vector error correction model (VECM) to test for long-run equilibrium. A bi-directional relationship was found among six stocks out of the seven HRITHIK stocks. This confirmed the causal relationship that futures prices have on the spot prices. The VAR Engel Granger causality test indicated that the spot market narrowly led the futures despite a bi-directional flow of information. The results from the VECM model proved that the futures market acted as the dominant market in the long-run. Usually, researchers have leveraged sector-wise stocks to provide insights into the futures market’s function in price discovery. For the first time, HRITHIK stocks were analyzed to examine the cause-and-effect relationship for individual stocks in India’s futures and spot markets. The study considered the pre-COVID 19 and the post-COVID 19 periods and investigated the impact of the pandemic on these stocks. The research used daily closing prices of HRITHIK stocks;however, intraday data could be more conclusive and accurate in revealing the dominant market. © 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved.

Is there a link between all-cause mortality and economic fluctuations?

Background: All-cause mortality is a global indicator of the overall health of the population, and its relation to the macro economy is thus of vital interest. The main aim was to estimate the short-term and the long-term impact of macroeconomic change on all-cause mortality. Variations in the unemployment rate were used as indicator of temporary fluctuations in the economy. Methods: We used time-series data for 21 OECD countries spanning the period 1960-2018. We used four outcomes: total mortality (0+), infant mortality (<1), mortality in the age-group 20-64, and old-age mortality (65+). Data on GDP/capita were obtained from the Maddison Project. Unemployment data (% unemployed in the work force) were sourced from Eurostat. We applied error correction modelling to estimate the short-term and the long-term impact of macroeconomic change on all-cause mortality. Results: We found that increases in unemployment were statistically significantly associated with decreases in all mortality outcomes except old-age mortality. Increases in GDP were associated with significant lowering long-term effects on mortality. Conclusions: Our findings, based on data from predominantly affluent countries, suggest that an increase in unemployment leads to a decrease in all-cause mortality. However, economic growth, as indicated by increased GDP, has a long-term protective health impact as indexed by lowered mortality.

Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model

We employed linear and nonlinear error correction models (ECMs) to predict the log returns of Bitcoin (BTC). The linear ECM is the best model for predicting BTC compared to the neural network and autoregressive models in terms of RMSE, MAE, and MAPE. Using a linear ECM, we are able to understand how BTC is affected by other coins. In addition, we performed Granger-causality tests on fourteen cryptocurrencies.

Vector Error Correction Modeling of COVID-19 Infected Cases and Deaths

The main objective of this paper is to investigate the dynamic relationship between the COVID-19 infected cases and the number of deaths due to COVID-19 using the Johnsen-Fisher co-integration test, vector error correction model and Granger causality test. The daily COVID-19-infected new cases and daily deaths due to COVID-19 in the United States, Canada, Ukraine and India were collected from the website for the period from 01-04-2020 to 26-12-2020. The summary statistics revealed that the highest numbers of COVID-19-infected cases were registered in the United States, followed by India, Canada and Ukraine;the highest numbers of deaths due to COVID-19 were registered in the United States, followed by India, Ukraine and Canada. The death percentage is exceedingly high in Canada, followed by the United States, Ukraine and India. The Johnsen-Fisher co-integration test results reveal the existence of one co-integration equation. The vector error correction model and Granger causality test reveal that long-term and short-term causality exists between COVID-19 infection and death cases. The speed of adjustment is found to be 9.9%. © 2022 NSP Natural Sciences Publishing Cor.

The Impact of COVID-19 Pandemic on Stock Market: Evidence from Romania.

The current health crisis has several socioeconomic influences that could be compared to those experienced during the 2008 economic and financial crisis. Governments around the world are making great efforts to sustain markets as there are signs showing that the health crisis will be followed by an economic crisis. In this study, we aim to investigate the impact of COVID-19 on the Romanian stock market. For this purpose, we considered the influence on the Bucharest Exchange Trading (BET) index of such variables as the number of new cases and the number of new deaths caused by COVID-19, measures taken by authorities, and the international economic context. The collected data covered the period between 11 March 2020 and 30 April 2021. The Autoregressive Distributed Lag (ARDL) Bound cointegration test was used to measure the impact of COVID-19 on the stock market. The results showed a significant long-term negative impact of the pandemic on the BET index for Romania, while the European economic context had a positive influence. Therefore, these results could be used by authorities as a good guideline for the efficient management of measures that aim to reduce the negative effects of the healthcare crisis.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.