Cointegration Relationship Research Articles

Role of economic growth, urbanization and energy consumption on climate change in Bangladesh

The present study empirically analyzes the impact of energy consumption, such as oil, gas, coal, gross domestic product, and urbanization, on environmental degradation in the form of CO2 emissions. The study objective is to identify the long-run and short-run interrelationships among the six key variables. The investigation is based on the time-series yearly data for the period of 1980–2020. We tested the stationarity for the variables using the Augmented Dickey-Fuller test and applied the Autoregressive Distributed Lag bounds model to investigate the cointegration relationship between the variables. The study also examined the short and long-run relationship to identify the variables’ causality impact over time. The results conclude that: (i) overall energy consumption and GDP have a unidirectional causality with CO2 emission in the long and short-run, which confirms that energy, economic growth and urbanization have an increasing impact on carbon emissions, (ii) the results also indicate for a short-run causality from energy, economy and urbanization and to CO2 emissions, (iii) the urbanization has a negative impact in the short term because of the industrialization, and (iv) the results conclude that Bangladesh should take stringent actions to control the surging CO2 emissions. Following the above conclusion, we highlight a few policy implications for enhancing green development in Bangladesh.

Does financial development improve economic growth? The role of asymmetrical relationships

This paper investigates the impact of financial development on economic growth in India, a major emerging economy. We estimate more flexible models than typically found to capture potentially asymmetric relationships while accounting for trade openness, foreign direct investment, and technological development. We document a cointegrating and asymmetric relationship between the key variables using nonlinear and standard autoregressive distributed lag models. We find a consistently negative impact of financial development and foreign inflows on economic growth in India in the long and short run, while trade liberalization and technological development have positive effects. Our findings, therefore, suggest caution regarding financial market liberalization in India.

Rare whiskies' market: new Eldorado for investors and collectors?

PurposeThis article aims to analyze the relationships between the different categories of rare whiskies and the opportunities for investors and collectors.Design/methodology/approachThis article uses indices representative of collectors, market performance and specific from 2008–2022 to a distillery and determine the returns. The author performs stationarity tests, cointegration procedures and the Granger non-causality test.FindingsThe results of this article indicates that average returns are positive. In addition, there is a wide range of annual returns, i.e. strongly negative and positive, leading to possible speculation over short periods. High and heterogeneous volatility accompanies these potential gains. The correlations between the different returns of rare whisky are close to zero, indicating potential gains in terms of portfolio diversification. This result is crucial for investors-speculators that benefit from an additional alternative asset. Cointegration relationships are more numerous in the short run than in the long run, confirming that rare whisky could present potential gains for investors, as collectors have in-depth knowledge of the relationships between the different markets.Originality/valueFinally, the author discusses the implications for different categories of economic actors (investors, collectors, sellers and producers).

Assessing the effect of the improvement of environmental damage compensation legal system and green finance project on the re-establishment of the ecological environment.

What are the relationships among environmental regulations, green finance, and environmental damages in countries? Existing literature supports the impact of green finance or green innovation on environmental quality, but rare studies query the cointegration among other core variables. We thus utilize the yearly data of 25 Chinese provinces from 2003 to 2021 to empirically examine the relationships among access to clean energy and technology, environmental regulation, renewable green investment, subsidy on green energy, and green finance index in environmental damage compensation via an augmented mean group (AMG) and other estimators. However, the current empirical research also investigates the individual linkage of green finance components with explained variables. Overall, this study confirms the existence of cointegration relationships among these variables. Moreover, the results of AMG suggest that access to clean fuels and technology, environmental regulations, and green finance can inversely affect the explained variable in the long term. Furthermore, environmental regulations and renewable green investment positively affect environmental damages, while a separate proxy of green finance also negatively affects explained variables in the selected provinces with better environmental performance. Our empirical findings offer important policy implications for overall emerging economies to promote subsidies, environmental regulations, and green finance to improve environmental damages compensation.

Measuring macroeconomic convergence and divergence within EMU using long memory.

This paper tests for convergence of EMU inflation rates and industrial production by testing for the existence of fractional cointegration relations. The notion of fractional cointegration allows for long-term equilibria with a higher degree of persistence than allowed for in the standard cointegration framework. Over the full sample from 1999:Q1-2021:Q4, we find evidence of fractional cointegration in both inflation and industrial production among many country pairs. Our results suggest some evidence for "convergence clusters" among either core or periphery countries in the case of inflation. Similarly, we find stronger evidence of cointegration pairs among core countries for industrial production compared to periphery or mixed core-periphery groups. Testing for a break in the persistence structure, the results show evidence of a break in the persistence of both inflation and industrial production in a number of countries. Inflation persistence is substantially higher after the break, suggesting a higher potential for diverging processes during economic crises. On the contrary, for the case of industrial production, persistence is lower during the post-crisis period.

A global scale COVID-19 variants time-series analysis across 48 countries.

The coronavirus disease (COVID-19) pandemic is slowing down, and countries are discussing whether preventive measures have remained effective or not. This study aimed to investigate a particular property of the trend of COVID-19 that existed and if its variants of concern were cointegrated, determining its possible transformation into an endemic. Biweekly expected new cases by variants of COVID-19 for 48 countries from 02 May 2020 to 29 August 2022 were acquired from the GISAID database. While the case series was tested for homoscedasticity with the Breusch-Pagan test, seasonal decomposition was used to obtain a trend component of the biweekly global new case series. The percentage change of trend was then tested for zero-mean symmetry with the one-sample Wilcoxon signed rank test and zero-mean stationarity with the augmented Dickey-Fuller test to confirm a random COVID trend globally. Vector error correction models with the same seasonal adjustment were regressed to obtain a variant-cointegrated series for each country. They were tested by the augmented Dickey-Fuller test for stationarity to confirm a constant long-term stochastic intervariant interaction within the country. The trend series of seasonality-adjusted global COVID-19 new cases was found to be heteroscedastic (p = 0.002), while its rate of change was indeterministic (p = 0.052) and stationary (p = 0.024). Seasonal cointegration relationships between expected new case series by variants were found in 37 out of 48 countries (p < 0.05), reflecting a constant long-term stochastic trend in new case numbers contributed from different variants of concern within most countries. Our results indicated that the new case long-term trends were random on a global scale and stable within most countries; therefore, the virus was unlikely to be eliminated but containable. Policymakers are currently in the process of adapting to the transformation of the pandemic into an endemic.

Validity of the Phillips curve in the Agricultural Sector: The Case of Turkey

Agriculture, which has historically been the main employment generating sector, is still very important in newly industrializing countries such as Turkey. Theoretically, it is thought that its share in GNP will decrease over time, but the depth of the decrease will depend on the economic impact of agriculture. One way to measure this effect is to adapt the Phillips curve for the agricultural sector. Therefore, the aim of this study is to analyse the validity of the Phillips curve in the Turkish agricultural sector. The study is conducted by the Augmented Dickey Fuller, Phillips Perron and Zivot-Anders Unit Root Tests as well as Nonlinear Autoregressive Distributed Lag and Autoregressive Distributed Lag tests via TURKSTAT 2014Q1-2020Q3 dataset. Reviewing these test, we find that the cointegration coefficient calculated by the NARDL model is negative, yet statistically significant. Accordingly, the short-term imbalances are eliminated in the long-run and the system converges to the long-run equilibrium values. Nonetheless, since the coefficient is greater than one, it is a sign of significant fluctuations in the convergence of the system to the long-term equilibrium. Additionally, the long-run negative cointegrated relationship of unemployment and inflation indicates that the Phillips Curve is valid in Turkish agricultural sector.

An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities

This study introduces an integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities with application to regional scrap steel prices in east, north, south, central, northeast, and southwest China. We use daily data and combine the Peter and Clark (PC) and Linear NonGaussian Acyclic Model (LiNGAM) algorithms to discover contemporaneous causalities. The vector error correction model shows each of the six series is part of cointegration relationships, and prices of north and southwest regions do not respond to long-run equilibrium disturbances. The PC algorithm shows the causal patterns, and the LiNGAM algorithm discovers the causal paths that enable the analysis of innovation accounting. Based upon impulse responses, we sort out sophisticated dynamics among price adjustment processes following shocks, for which north and southwest regions dominate price adjustment processes compared to the other four regions. The proposed approach provides short-term and long-term pricing policies for scrap steel throughout China.

Impact of agricultural credit disbursement on cereals yield in Odisha

AbstractRealizing the significance of agricultural credit and considering millets and rice are the major crops in Odisha, this study proposes to examine the impact of agricultural credit disbursement (crop loan and term loan) on the yield of total cereals, millets and rice for the period 2000–01 to 2019–20. To examine the cointegration relationship among the variables, an autoregressive distributed lag (ARDL) bound F‐test, and to investigate the impact of agricultural credit on yield of total cereals, millets and rice, an ARDL regression modelling framework is employed. The empirical result for the bounding F‐test provides a statistically significant relationship between agricultural credit and yield of total cereals, millets and rice at 1% level, confirming the long‐run equilibrium relationship in the models. The long‐run impact of crop loans positively affects the yield of total cereals and rice, whereas it has no statistically significant effect on the yield of millets. Moreover, the change in term loan negatively affects the yield of total cereals, millets and rice in the long‐run, where the magnitude is higher in the rice yield compared to the yield of millets. However, in the short‐run, both crop and term loans negatively affect the yield of total cereals, millets and rice. Based on the findings of this study, few policy implications have been suggested to reduce the negative consequences of agricultural credit on cereal yields in Odisha.

The Relationship between Bank Deposits and Macroeconomic Variables in Ghana: A Co-Integration Approach

This study examined the linkages between macroeconomic variables and how those relationships affect the total deposits of Ghanaian banks. The macroeconomic variables included in this study were Inflation (I), Monetary Policy Rate (MPR), Gross International Reserve (GIR), Public Debt (PD), Gross Domestic Product (GDP), GSE All share Index (GASI), Rate of change in Total Money Supply (M2+), deposits in the banking sector (TD). The study employed monthly data over the period (2015–2020); obtained from the Bank of Ghana monthly time series database. The data were analyzed using Gretl. The cointegration technique was employed in this study to gauge the long-term and short-term responsiveness of the connections. The ADF results indicated that the study variables were non-stationary. The econometric analysis suggested that the study variables, inflation (I), Gross Domestic Product (GDP), Public Debt level (PD), and Total Deposits (TD) in banks operating in Ghana, exhibited a significant positive long-run cointegration relationship. This suggested that the identified variables play a crucial role in explaining the fluctuations in total deposit levels within the Ghanaian banking industry. Bank deposit is strongly exogenous and moves to restore equilibrium through several short-run partial adjustments in the short-run. Also, in the short-run, only the GSE All-share index (GASI) significantly influenced bank deposits, but not in the long-run. In the long run, the relationship was still positive but insignificant.

The dynamic relationship between trade openness, foreign direct investment, capital formation, and industrial economic growth in China: new evidence from ARDL bounds testing approach

The objective of this research is to use annual data from 1990 to 2021 to examine the long- and short-run dynamic relationships among China’s trade openness (TRO), foreign direct investment (FDI), capital formation (K), and industrial economic growth (IEG) using the Autoregressive Distribution Lag (ARDL) method. Firstly, the results of the ARDL co-integration tests show that there is a long-run co-integration relationship among TRO, FDI, K, and IEG. Secondly, from a path of influence perspective, both the long- and short-run relationships are almost the same. Specifically, TRO, FDI, and K all have positive effects on IEG and vice versa, which supports the feedback hypothesis. However, contrary to the short-run relationship, TRO and K have a small negative effect on IEG, but this is not statistically significant. Finally, K and TRO positively affect FDI, while FDI negatively affects K, although the effect is minimal and negligible at the 10% significance level. On the contrary, they are not statistically significant in the long run. These results support the theory that technological innovation in the trade, investment and capital system based on economic and market capital can stimulate the development of China’s industrial economy.

The dynamic link between eco-innovation and ecological footprint in India: does the environmental Kuznets curve (EKC) hold?

PurposeThe eco-innovation is considered one of the possible ways to tackle climate change. However, the conflicting empirical evidence related to the role of eco-innovation on environmental quality becomes a motivation to explore the effect of eco-innovation on environmental degradation proxied by ecological footprint. Besides, it controls economic growth, remittance inflows, trade openness and total energy consumption in the environmental degradation function.Design/methodology/approachUses the Augmented Auto Regressive Distributed Lag (AARDL) approach to examine the cointegration relation among the series during the period ranging from 1975 to 2017 for India within the environmental Kuznets curve (EKC) framework.FindingsThe result suggests that eco-innovation can mitigate climate change by reducing the ecological footprint. Similarly, economic growth reduces the ecological footprint in the short- and long-run. However, the square of economic growth is positive and significant. Thus, it shows evidence against the conventional EKC hypothesis. The results also reveal that remittance inflows have an insignificant negative role on the ecological footprint, while total energy consumption and trade openness harm the environment by enhancing the ecological footprint.Practical implicationsThis study provides important implications for climate change mitigation. Thus, the government should promote eco-innovation to mitigate climate change by offering a favorable legal environment to the firms to adopt the same in their production and consumption activities. It also suggests that initiatives like green strategies should give serious attention while incurring research expenditure.Originality/valueNo prior studies assess the impact of eco-innovation on the ecological footprint for the period of 1975–2017 in India.

Does excessive energy utilization and expansion of urbanization increase carbon dioxide emission in Belt and Road economies?

The phenomenal increase in global temperature and variation in climate change are the replications of nature, alarming governments to limit the emissions of greenhouse gases (GHGs) and adopt green innovation and environmental-friendly clean and green technologies. In this paper, we empirically investigate whether there are any changes in excessive consumption of energy from conventional sources, expansion of urbanization, carbon dioxide (CO2) emissions, and economic growth in six different regions, namely, East Asia (EA), South Asia (SA), Southeast Asia (SEA), Central Asia (CA), Eastern Europe (EE), and the Middle East and North Africa (MENA), under the Belt and Road Initiatives of panel data over the period of 1985 to 2017. The empirical methods include a panel co-integration check, heterogeneity test, panel Granger causality test, pooled mean group (PMG), and augmented mean group (AMG). To verify the outcomes, robustness tests were carried out using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Our results confirm that CO2emissions are primarily influenced by excessive utilization of conventional energy, economic growth, and expansion of urbanization. The findings confirm the co-integrating relationships among the variables in all six regions. Moreover, the panel causality analysis identified a bidirectional causal relationship between energy consumption, economic growth, urbanization, and CO2emissions. While these results can play an instrumental role in formulating CO2emission policies among our selected countries, our research can also assist policymakers and governments in other developing countries implement important policy initiatives. In this regard, the findings suggest that the current environment-related polices of Belt and Road Initiatives (BRI) do not efficiently tackle CO2 emissions. In order to achieve the CO2 emission degradation objective, the Belt and Road countries should restructure their environment-related policies by limiting the consumption of conventional energy and expansion of urbanization. The adaptation and establishment of such a panoramic policy program can assist emerging economies to acquire consolidated and environmentally sustainable economic growth.

The determinants of non-traditional agricultural exports’ growth in Ghana

The Nontraditional Agricultural Exports (NTAE) sub-sector is a key contributor to agricultural gross domestic product (GDP) in several developing countries. It has the potential to create jobs and contribute to enhancing food and nutrition security, hence an important pillar to the economies of several countries including Ghana. This study, therefore, examines the factors driving the growth of nontraditional agricultural exports in Ghana using an Autoregressive Distributive Lag (ARDL) model. The study was based on secondary data from the World Development Indicators (WDI) and Food and Agriculture Organization (FAO). The study identified real GDP to be a significant determinant of NTAE’s growth in both contemporaneous and lag forms. Other variables including inflation, gross capital formation and foreign direct investment influence NTAE’s growth through their lag values. Exchange rate, inflation and RGDP significantly determine the growth of NTAEs in the long-run. The study further identified a co-integrating relationship between NTAE and other explanatory variables. The Error Correction Model showed an adjustment speed of 0.41, indicating that 41% adjustment will occur within the first year and it will take 2.4 years for the system to return to equilibrium in the long-run. Based on the findings, the study recommends that Ghana Export Promotion Authority (GEPA) adopts promotional strategies including providing incentive packages to farmers, exhibitions and trade fairs to curtail the declining trend in the growth of the NTAE’s sector. The Ministry of Food and Agriculture (MoFA) should educate farmers on the use of acceptable agronomical practices in the production field. Lastly, Ghana Investment Promotion Center (GIPC) and MoFA should collaborate to identify strategic areas for domestic support and foreign direct investment in the NTAE’s sub-sector to ensure it grows and contributes towards socio-economic development in Ghana.

TRANSPARENCY, GOVERNANCE AND FISCAL SUSTAINABILITY NEXUS: THE NIGERIA SCENARIO

The problem of fiscal unsustainability has become pronounced in Nigeria. The role of policy makers and the availability of essential information on the economy seem to be major causes of this ominous predicament. This paper aimed at investigating the causal relationship between transparency, governance and fiscal sustainability in Nigeria from 1996 to 2021, using a vector auto regression framework. The empirical results confirm a cointegration relationship between transparency, governance and fiscal sustainability. The flow of causality between control of corruption and fiscal sustainability appears to be mixed. At 1 lag, control of corruption granger causes fiscal sustainability, while at 2 lags, fiscal sustainability granger causes control of corruption at 5% and 10% significance levels. Results of the impulse response functions suggest that one standard deviation innovation on fiscal sustainability reduces fiscal deficit in the medium and long term, while results of the variance decomposition indicate that a significant variation in Nigeria’s fiscal sustainability is not attributable to changes in the political stability. It is recommended that for fiscal sustainability to be improved and sustained, mechanisms which deliberately seek to enhance improved level of fiscal discipline in the country should be employed. It is also imperative for more institutionalized policies that can improve transparency and promote inclusive governance that can curb fiscal indiscipline like budget deficit in view of stimulating fiscal sustainability in Nigeria.

Determining the Impact of Foreign Direct Investment on Exports of Pakistan using ARDL Bounds Testing Approach

This study employed autoregressive distributed lag model (ARDL) approach to investigate how exports and foreign direct investment (FDI) are related in Pakistan for the period spanning from 1974 to 2019. For cointegration analysis, we used bounds test. The findings of the cointegration test indicated that the variables have a long-term cointegration relationship. According to estimated results, the relationship between the two variables is negligible over the long-run, whereas FDI has a positive influence on exports in the short-run. Moreover, the estimated error correction term is significant with the expected sign. It is concluded that FDI inflows are advantageous for improving manufacturing processes, which would eventually lead to high-quality exports and economic progress of Pakistan. Policymakers are required to make efforts to removing all hurdles in Pakistan's economic progress.

Resource curse hypothesis in COP26 perspective: Access to clean fuel technology and electricity from renewable energy

Most of the previous research on natural resources remains a dilemma in whether the natural resources extraction mend the environmental sustainability or deteriorates it. To consider this issue, we investigate the causal relationship between environmental sustainability, natural resources, economic performance, renewable energy and technology nexus for the Organization for Economic Co-operation and Development countries. To this time, the CO2 emissions serve as a proxy for environmental sustainability, wrapping the period 1989–2021. Hence, the study is based on time series data, advanced cointegration and long-term relationship methods and Granger causality are used. Furthermore, diagnostics and stability tests have also been employed along with the unit root approaches to account for the different structural breaks in the data. The status of the data based on normality is fine and represents that data is normally distributed, requiring only the long-term association of the variables with each other. The study's empirical findings indicate the positive impact of natural resources on environmental sustainability, which shows the resource blessings. The EKC hypothesis also exists in the OECD economies because of the inverse relationship with CO2 emissions. Moreover, the results of renewable energy and cleaner technology are negative, which indicates that despite the up gradation in the environmental sustainability rankings under the targets of COP21 and 26, the usage of renewable energy will still impact the industrial and production sectors of OECD economies. This study suggests the results of the study to policymakers and practitioners the efficient use of electricity and innovative technology to stop the depletion of natural resources further, a convergence of non-renewable energy to renewable energy and reassurance of environmental sustainability under the targets of COP26.

Economic policy uncertainty, renewable energy and environmental degradation: Evidence from Egypt

This study contributes to the growing but still limited body of literature on the impact of economic policy uncertainty, renewable energy usage, and economic growth on environmental degradation in Egypt. Using the autoregressive distributed lag (ARDL) bound test, we examine the existence of cointegration relationships in Egypt over the period 1990–2018. Our results indicate that economic policy uncertainty is positively associated with environmental degradation in both the short and long run. Additionally, we find that economic growth exacerbates environmental degradation in both the short and long term. Finally, renewable energy consumption has a significant negative effect on environmental degradation in the long run. Therefore, ensuring economic policy stability is crucial for maintaining environmental quality. To this end, Egyptian policymakers should prioritize low-carbon research and development techniques, as well as the adoption of renewable energy sources to mitigate environmental degradation.

Epidemiological characteristics, seasonality, trends of dog bite injuries, and relationship with meteorological data.

Animal bites are among the most critical in public health problems. Dogs are the leading cause of bite injuries. The study aimed to investigate the epidemiology and clinical features of dog bite cases admitted to an emergency department, as well as their temporal trends, seasonality, and tr relationship with meteorological data. Study data comprised eight years (2012-2019) emergency room records of a tertiary center. Demographic characteristics of the cases, bite anatomical area, treatment applied, hospitalization, and death rates were determined. The incidence rates and distribution of meteorological data by years were examined using ANOVA and Kruskal Wallis tests. Seasonality and temporal trends were investigated for incidence rates using the additive decomposition technique. The temporal relationship of incidence rates with meteorological data was evaluated using the Autoregressive Distributed Delayed Boundary Test. Causality verification was perfoirmed using the Granger test. Dog bite cases consisted of 1,335 records of partients with a mean age of 26.6±0.2 years. Bite cases were most common in the 20-44 age group (44.7%), males (76.4%), and lower extremities (48.2%). The frequency of hospitalization was 4.1%. Annual incidence rates ranged from 52.7-49.9/100,000, with a non-significant increasing trend. The incidence of bites had two peaks, in June and August. A co-integration relationship was observed between incidence rates and air temperature and humidity levels (p<0.001). Effective implementation of prevention programmes is needed for high-risk demographic groups. In addition, a national monitoring and reporting system could evaluate the effectiveness of any prevention programme and reduce the incedence of dog bites.

Kemiskinan dan Faktor-Faktor yang Mempengaruhinya di Indonesia

Poverty alleviation in Indonesia is strongly influenced by other economic variables. The three main factors used to measure its effect on poverty are economic growth, unemployment and inflation. This study aims to examine and analyze the relationship between theoretical and empirical balance between economic growth, unemployment rate and inflation rate with poverty in Indonesia both in the short and long term. The estimation method used is a dynamic econometric model with the cointegration approach and the Error Correction Model (ECM). The results of this study indicate that the equation model used has cointegration relationships and long-term balance between variables. The estimation results show that there is a short-term effect of economic growth, the unemployment rate and inflation on poverty, while in the long term economic growth and the unemployment rate have a significant effect, while inflation is not significant.