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This study examines the impact of macroeconomic variables in addition to the COVID-19 pandemic on tariffs in Jordan for the period 2003–2020, by using Autoregressive Distributed Lag (ARDL) model, in order to reveal the long-run relationship between macroeconomic variables and tariffs. However, it became clear that all these macroeconomic variables contributed to tariffs in Jordan, and it was proven that there is a long-term relationship between them and tariffs. On one hand, GDP negatively affected tariffs in Jordan, on the other hand, imports and COVID-19 pandemic positively affected tariffs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study examines the impact of macroeconomic variables in addition to the Corona pandemic on bank lending in Jordan during 2008:01–2021:02, and thus using the Autoregressive Distributed Lag (ARDL) model, in order to reveal the long-term relationship between macroeconomic variables and bank lending. However, it became clear that all these macroeconomic variables contributed to bank credit in Jordan, and it was proven that there is a long-term relationship between them and bank credit. On one hand, the GDP, inflation, interest rate and coronavirus pandemic negatively affected the lending activities of commercial banks in Jordan, on the other hand, inflation had positively affected these activities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Expansionary monetary and fiscal policies implemented after the economic contraction brought on by the covid-19 pandemic and the ensuing global crisis caused the debt sustainability problem to become the primary concern of all national economies. Consequently, analyzing India, Brazil, Mexico, South Africa and Turkey has become more important as these countries have structural weaknesses in their balance of payments. In this study, the debt sustainability of these countries, which are also known as the fragile five due to their similar problems in macroeconomic variables, was analyzed for the period of 2001-2019. The paper adopts the methodology developed by Hakkio and Rush (1991), Sawada (1994), and onel and Utkulu (2006). In this context, the cointegration relationship between the variables that cause and decrease external debt was examined using the Panel ECM test (Westerlund, 2007). This paper utilzies countries' trade balance, change in central bank currency reserves, net public transfers and interest rate (Libor and CDS) on total debt as variables. Results of the panel cointegration test show that the countries' sustainability of external debt is weak. The country-level analyses show that only Mexico has strong external debt sustainability and Turkey, having the highest CDS premium among the five, has the weakest external debt sustainability
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The recent decade has seen a rapid rise in risk assets. Stocks, commodities, and cryptocurrencies have exploded to the upside. Global central banks have maintained interest rates at record low levels following the COVID-19 crisis. This has further acted as tailwinds for risky assets. With asset classes being increasingly interlinked with each other, useful information can be gained by studying these inter-relationships. This paper looks at the interrelationships between the Indian stock market Nifty index and some key asset classes such as Gold, Crude oil, short-term and long-term Indian government bond yields, the USD/INR exchange rate, and the cryptocurrency Bitcoin for the period January 2011 to December 2020. Co-integration analysis suggests the absence of long-run relationships between the Nifty and the asset classes studied. Granger causality analysis reveals bi-directional causality between Nifty and USD/INR and Crude oil returns. Gold returns, Bitcoin returns, and changes in short and long-term government bond yields uni-directionally granger-caused Nifty returns. Impulse response analysis reveals that shocks in each of the independent variables caused a shock in the Nifty that persisted for 1 to 3 weeks. Traders in the Nifty can monitor these shocks and accordingly fine-tune their strategies for possible moves in the Nifty. © 2022 ACM.
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The current study gains an insight into the determinants of economic growth (gross fixed capital formation, labor force, trade openness, economic freedom, and governance) taking into consideration the effect of the Covid-19 pandemic. It employs a panel co-integration methodology in Jordan over the period 1975–2020. The results show that gross fixed capital formation, labor force, trade openness, economic freedom, and governance indicators had major positive impacts on economic growth. The Covid-19 pandemic has had a substantial negative effect on economic growth. On the basis of the results, the government of Jordan ought to perform real institutional reforms and policies to promote human rights, and improve the rule of law, ensuring that resources are used efficiently and effectively in the pursuit of economic development. In addition, the promotion of trade such that new knowledge and leading technologies can be transferred, labor and capital productivity is increased, domestic and foreign competition is encouraged, and more international financial flows and investments are attracted. to the best of the author's knowledge and belief, the current study is the first to examine the determinants of economic growth in Jordan over the period, 1975–2020, taking into consideration the effect of the Covid-19 Pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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FDI flows to Africa reached $83 billion a record level in 2021, from $39 billion in 2020, due to a large corporate reconfiguration in South Africa. Global FDI flows in 2021 were $1.58 trillion, up 64 per cent from the level during the first year of the COVID-19 pandemic of less than $1 trillion. During 1994-2020, Africa, FDI growth was registered about 1.64 per cent per annum and significant at 1 per cent level. In Africa, all the regions having positive growth rate and statistically significant at 5 per cent level. In this paper, employ Johansen co-integration as well as Granger Causality test' for FDI and GDP during 1994-2020, before examining these two tests we used Augmented Dickey-Fuller (ADF) test for the stationarity. From the ADF test, the tests were carried out for the series LnFDI and LnGDP. The results shows that LnFDI, and LnGDP are non-stationary at level, but the first difference appropriate significant level. Johansen Co-integration test results reveals;FDI and GDP have long run Co-integration in the entire Africa continent, Central and North Africa regions, remaining, there is no co-integration is accepted. From the Granger causality test results indicates Africa and Southern Africa region, LNGDP is LNFDI granger reason, and also LNFDI is LNGDP granger reason, which indicates that there is direct Granger causality relationship between total Africa continent FDI & GDP and also GDP & FDI. In western Africa region, there is direct Granger causality relationship between GDP and FDI, but there is no Direct Granger causality relationship between FDI and GDP. Copyright © 2022, Anka Publishers. All rights reserved.
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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.
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Purpose: The purpose of this paper is to find out the relationship between price of Gold, price of Crude Oil, Exchange Rate of India, and Indias stock market. The research has been done on Pre-COVID time periods to analyse the relationship in scenarios like pre-global financial crisis, during crisis and post crisis. The authors incorporate the data from pre-crisis phases i.e., 2005 to 2019, to find out the relationship between the variables using Granger causality test, Johansens Cointegration, and Vector Autoregression. To study the spill-over effect on Indias stock market, regression has been used. The empirical results indicate that for the Pre-Crisis and Post-Crisis periods, Gold does granger cause USDINR, for all three periods Crude oil does granger cause Gold, for the crisis and post crisis periods Gold does granger cause Crude oil, for the post-crisis period USDINR does granger cause Crude oil. No other causality relationship was established with the help of this empirical analysis. Johansens cointegration test revealed that no cointegration exists amongst the three variables. The impact of exchange rate on Indias stock market has changed as compared to the previous time periods. The exchange rate was inversely related to the stock markets for the Pre-Crisis and Crisis periods and is directly related to the stock market for the Post-Crisis period. This study adds to the existing literature on the variables, by using phase-wise data and performing empirical analysis to find out the relationship between the variables. Not many literatures demonstrate together the relationship among these three variables in three different periods. This is a significant gap that the study aimed to address.
ABSTRACT
This study examines the impact of macroeconomic variables in addition to the COVID-19 pandemic on tariffs in Jordan for the period 2003–2020, by using Autoregressive Distributed Lag (ARDL) model, in order to reveal the long-run relationship between macroeconomic variables and tariffs. However, it became clear that all these macroeconomic variables contributed to tariffs in Jordan, and it was proven that there is a long-term relationship between them and tariffs. On one hand, GDP negatively affected tariffs in Jordan, on the other hand, imports and COVID-19 pandemic positively affected tariffs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This study examines the impact of macroeconomic variables in addition to the Corona pandemic on bank lending in Jordan during 2008:01–2021:02, and thus using the Autoregressive Distributed Lag (ARDL) model, in order to reveal the long-term relationship between macroeconomic variables and bank lending. However, it became clear that all these macroeconomic variables contributed to bank credit in Jordan, and it was proven that there is a long-term relationship between them and bank credit. On one hand, the GDP, inflation, interest rate and coronavirus pandemic negatively affected the lending activities of commercial banks in Jordan, on the other hand, inflation had positively affected these activities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study examines the strength of the impact of fiscal policy tools on economic wellbeing as measured by per capita income in Malaysia from 1996 to 2020. The impact of fiscal policy instruments on economic wellness, represented by real income per capita, is measured using the autoregressive distributed lags model. The speed of adjustment from short-run disequilibrium to long-run equilibrium is also measured to assess the strength of the fiscal instruments' impact on per capita income. Empirical results exhibit the existence of co-integration relationships between per capita income, tax revenue, and government spending. The findings provide strong support for the presence of a long-run positive impact on government spending and a long-run negative impact of tax revenue on per capita income. The coefficient of ECTt-1 indicates that deviations from a short-run disequilibrium to a long-run equilibrium from the current to the future period are corrected with a speed of 76% (equivalent to a duration of 1.5-2 years to return to equilibrium). The practical and policy implication of the results is fiscal instruments play a significant role, mainly in alleviating the economic impact of the COVID-19 pandemic in the long run.
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This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.
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Purpose>Achieving the goals of the sustainable development strategy and Egypt’s vision 2030 depends mainly on the existence of sources of funds. And since Egypt faces a great challenge in obtaining finance, then analyzing the drivers of financial development is a vital issue and there is a persistent need to shed light on the key obstacles for it. Thus, this paper aims to empirically assess the impact of natural resources, foreign direct investment (FDI) net inflows, education and clean energy sources on financial development in Egypt using the data of the 1971–2014 period.Design/methodology/approach>The paper uses auto-regressive distributed lag and Toda-Yamomoto approaches to fulfill the purpose.Findings>Empirical results signify that all variables except natural endowments stimulate financial development which can suggest the presence of the natural resources curse in Egypt. Moreover, the feedback effect between financial development and FDI is recognized. Clean energy sources cause financial development and natural endowments. Financial development causes natural endowments and FDI leads to the deployment of more clean energy resources.Practical implications>Several crucial policy implications are suggested based upon these results as improving the quality and quantity of education and encouraging both domestic and foreign investors by providing several incentives. Moreover, the government has to enhance green finance through financing solar energy projects and other environmentally friendly projects.Originality/value>It is the first research for Egypt that explores natural resource-financial development nexus using time series analysis according to our information, and two important variables are included in the model which is clean energy sources and FDI. Then, although several studies examined the impact of financial development on clean energy no empirical study before assessed the impact of clean energy on financial development.
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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.
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With the outbreak of COVID-19, a lot of studies have been carried out in various science disciplines to either reduce the spread or control the increasing trend of the disease. Modeling the outbreak of a pandemic is pertinent for inference making and implementation of policies. In this study, we adopted the Vector autoregressive model which takes into account the dependence that exists between both multivariate variables in modeling and forecasting the number of confirmed COVID-19 cases and deaths in Nigeria. A co-integration test was carried out prior to the application of the Vector Autoregressive model. An autocorrelation test and a test for heteroscedasticity were further carried out where it was observed that there exists no autocorrelation at lag 3 and 4 and there exists no heteroscedasticity respectively. It was observed from the study that there is a growing trend in the number of COVID-19 cases and deaths. A Vector Autoregressive model of lag 4 was adopted to make a forecast of the number of cases and death. The forecast also reveals a rising trend in the number of infections and deaths. The government therefore needs to put further measures in place to curtail the spread of the virus and aim towards flattening the curve. © 2021 WITPress. All rights reserved.
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The present research study examines the impact of Stock marketson Gold prices using daily data for pre and during COVID-19 period (January-October 2020). This study uses Unit root test, Granger causality test, GARCH method and Johansen's co-integration test to evaluate difference in the Volatility as well as the relationship between them. The findings show that no causal relationship exists between Gold Prices and Stock market prices in the short run. The result of the Johansen Co-integration test for the long-run relationship between theGold price and Nifty Indices showno co-integration at all, but low co-integration inshort-run cannot be ruled out. With this study, an attempt has been made to reveal the relationship that exists between Gold and stock markets with empirical findings using the time series analysis which reveals the original side of work during the pandemic. The ARCH and GARCH coefficient explain significantly the persistence of information on stock return volatility. The present study recommends that the integration between Gold and Stock market price entails the need for investors globally to follow a portfolio stock selection strategy to add value from the investments in India.These findings have important implication for the investors seeking portfolio diversification. © 2021. Transnational Press London. All Rights Reserved.
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This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors.