Cointegration Relationship Research Articles

RENEWABLE ENERGY AND THE STATE OF ENVIRONMENTAL QUALITY IN NIGERIA

The study aimed at examining the effect of renewable energy on quality of the environment in Nigeria from 1980 to 2019. The ARDL bound test of cointegration shows the presence of a cointegration relationship among the analyzed variables. In the long run renewable energy is related negatively with to CO2 emissions, meaning that an increase in renewable energy enhances the quality of the environment by reducing the number of CO2 emissions in the long run while in the short-run renewable energy is positively related to carbon emissions, meaning that increases in the number of renewable energy deteriorate the quality of environment in the short run. The Granger causality test proposed by Toda and Yamamota (1995) reveals that a bidirectional causal relationship exists between renewable energy and the number of CO2 emissions, meaning that changes in renewable energy affect the quality of environment and at the same time changes in the quality of environment affect the renewable energy in Nigeria. Policymakers should implement a multilateral mechanism to ensure energy efficiency and to promote the use of renewable energy.

Temporal tendency, seasonality and relationship with climatic factors of Crimean-Congo Hemorrhagic Fever cases (East of Turkey: 2012–2021)

Crimean-Congo Hemorrhagic Fever continues to be an important public health problem by expanding its borders. To evaluate the temporal trend, seasonality, and relationship with the climatic factors of Crimean-Congo Hemorrhagic Fever. Study data included cases treated in two different tertiary healthcare institutions between 2012 and 2021. The demographic characteristics of the cases and the dates of admission to the hospital were determined, and they were matched with the average of the measurements (temperature, cumulative precipitation, relative humidity, wind speed) of two different meteorology stations in the study area. By calculating the crude incidence rates, the trend in years was investigated. Estimates were created by removing the incidence rates, seasonality, and trend components using the additive decomposition technique. The temporal relationship between incidence rates and climatic factors was evaluated with the help of the Autoregressive Distributed Lag Bound Test. Toda Yamamoto test was used for causality verification. The mean age of the cases (n = 974) included in the study was 47.6 ± 17.7 years, and the majority (57.3%) were in the group above 45 years of age. 56.6% of the cases were male and there was a male predominance in all age groups. Incidence rates ranged from 5.5 to 23.1/100,000 over the ten-year period and there was a significant upward trend (R2 = 0.691, p = 0.003). Cases of Crimean-Congo Hemorrhagic Fever that started in March, peaked in July and ended in October, showed a clear seasonality. A cointegration relationship was observed between case incidence rates and air temperature, cumulative precipitation, and relative humidity (p < 0.05 for all). Climatic factors can only indirectly affect the occurrence of Crimean-Congo Hemorrhagic Fever cases. However, climatic conditions that become progressively more favorable for vector ticks lead to the spread of the disease. The control measures to be taken should be prepared by considering the changing climatic conditions and prioritizing the risk groups. There is a need for information and awareness-raising studies about climate change and the growing dangers associated with it, also outside of endemic regions.

THE IMPACT OF ISLAMIC FINANCING PRODUCTS ON ISLAMIC BANKS PROFITABILITY

This study aims to determine the effect of Musharaka, Murabaha, Istisna’a and Ijarah financing on the profitability of Al Rayan Islamic Bank. This research uses Quarterly data of Al Rayan Bank for 2011Q1-2023Q1. The data is taken from the financial statements of income and statements of financial position. The variables used in this study are independent variables, Musharaka, Murabaha, Istisna’a and Ijarah financing. While the dependent variable is, the Islamic bank's profitability measured by Return on Equity (ROE).The study used ARDL and Error Correction Models to analyze the short and long-term relationships between variables. Data collected were analyzed by using EViews10 software. ARDL bounds test findings shown that there is a cointegration relationship between bank profitability and Islamic financing Products variables in the long run. The results of the long-run analysis show that Musharaka financing has a positive and significant effect on ROE at the 5% level. Murabaha financing has a negative and significant effect on ROE. Meanwhile, the Istisna’a and Ijarah variables have no significant effect on ROE. The short run dynamics show that there is a negative and significant effect of Murabaha and Ijarah financing on the profitability of the Islamic bank, while lagged value of Murabaha LMUR(-1) has a positive and significant effect on the profitability index of the Islamic bank. Simultaneously, there is no relationship and effect of Musharaka and Istisna’a on the profitability of the Islamic bank in the short term.

Econometric study on the impact of digital technologies on GDP in Algeria

The aims of this study are the concept of digital impact economy technologies on the gross domestic product in Algeria in the last twenty years. We link our study to using of the directed error correction model (VECM). This study concluded that there is a single relationship of co-integration in the long term between the impact of digital economy technologies and human resource management through the effect of both fixed-line subscribers and the number of telephone subscribers in the gross domestic product. Results can reach that the contribution of digital economy technologies is for the increased economic growth.

Cointegration and causality relationship of Indian stock market with selected world markets

Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.

More Accurate Climate Trend Attribution by Using Cointegrating Vector Time Series Models

Adapting to human-induced climate change is becoming an increasingly important aspect of sustainable development. To be able to do this effectively, it is important to know how much human influence has contributed to observed climate trends. Climate detection and attribution (D&amp;A) studies achieve this by estimating scaling factors usually obtained by performing a least squares regression of the observed trending climate variable on the equivalent variable simulated by a climate model. This study proposed instead to estimate scaling factors by using the econometric approach of dynamically modelling the time series as a cointegrating Vector Auto-Regressive (VAR) time series process. It is shown that a 2nd-order cointegrating VAR(2) model is theoretically justified if the observed and simulated variables can be represented as a one-box AR(1) response to a common integrated forcing. The VAR(2) model can be expressed as a Vector Error-Correction Model (VECM) and then fitted to the data to obtain the cointegration relationship, the stationary linear combination of the two variables, from which the scaling factor is then easily obtained. Estimates of the scaling factor from the VAR(2) model are critically compared to those from Ordinary Least Squares (OLS) and Total Least Squares (TLS) for annual Global Mean Surface Temperature (GMST) data simulated by a simple stochastic model of the carbon–climate system and for historical simulations from 16 climate models in the Coupled Model Intercomparison Project 5 (CMIP5) experiment. Results from the toy model simulations show that the slope estimates from OLS are negatively biased, TLS estimates are less biased but have high variance, and the VAR(2) estimates are unbiased and have lower variance and provide the most accurate estimates with smallest mean squared error. Similar behaviour is noted in the CMIP5 data. Hypothesis tests on the VAR(2) fits found strong evidence of a cointegrating relationship with the observations for all the CMIP5 simulations.

A coordination analysis on tourism development and resident well-being in the Yangtze River Delta Urban Agglomeration, China

Based on the coupling coordination degree model (CCDM), this study examines the spatio-temporal characteristics of the coupling coordination of tourism development and resident wellbeing in Yangtze River Delta Urban Agglomeration (YRDUA), China. The results showed that the both tourism development and resident wellbeing in YRDUA were steadily increasing from 2000 to 2019. Shanghai City enjoyed the highest level of condition, followed by Zhejiang Province, Jiangsu Province, and Anhui Province. Second, the coupling coordination of tourism development and resident wellbeing was in a basic coordinated state. Moreover, the coupling coordination degree (CCD) of each city was gradually increasing and the spatial difference was gradually decreasing during the sample period. Third, there was a long-term and stable cointegration relationship between tourism development and resident wellbeing. Compared with the impact of tourism development on the resident wellbeing, the growth of resident wellbeing played a more important role in promoting tourism development. Policy implications were then generated to promote high-quality tourism development and the improvements of resident wellbeing for YRDUA and other urban agglomerations in China and of the world.

Financial development as a new determinant of energy diversification: The role of natural capital and structural changes in Australia

This paper investigates the relationship between financial development and energy diversification for Australian economy by considering natural capital, structural changes, economic growth, and export diversification as additional determinants of energy diversification demand function. The bootstrapping ARDL cointegration approach is applied for examining cointegration relationship between energy diversification and its determinants by considering presence of structural break stems in the series. Our empirical analysis reveals that financial development affects energy diversification positively. Natural capital is positively linked with energy diversification. Structural changes have favourable effect on energy diversification. Economic growth declines energy diversification. The relationship between export diversification and energy diversification is negative. Financial development and energy diversification have a nonlinear relationship between with an inverted-U shaped. The bootstrapping ARDL Granger causality analysis reveals the presence of feedback effect between financial development and energy diversification. This empirical analysis provides new policy insights for Australian policy makers using financial development as economic tool to boost up energy diversification.

Adaptive Cointegration Analysis and Modified RPCA With Continual Learning Ability for Monitoring Multimode Nonstationary Processes.

This study investigates nonstationary process monitoring under frequently varying modes, where new modes are allowed to emerge constantly. However, in current multimode process monitoring methods, generally, data are required from all possible modes and mode identification is realized by prior knowledge for multimode nonstationary processes. In contrast, recursive methods update a monitoring model based on the successive data. However, they forget the learned knowledge gracefully and fail to track drastic variations. Aimed at nonstationary data in each mode, this article proposes an adaptive cointegration analysis (CA) to distinguish real faults from normal variations, which updates a model once a normal sample is encountered and adapts to the gradual change in the cointegration relationship. Then, a modified recursive principal component analysis (RPCA) with continual learning ability is developed to deal with the remaining dynamic information, wherein elastic weight consolidation is adopted to consolidate the previously learned knowledge when a new mode appears. The preserved information is beneficial for establishing a more accurate model than traditional RPCA and avoiding drastic performance degradation for future similar modes. In addition, novel statistics are proposed with prior knowledge and thresholds are calculated by recursive kernel density estimation to enhance the performance. An in-depth comparison with recursive CA and recursive slow feature analysis is conducted to emphasize the superiority, in terms of the algorithm accuracy, memory properties, and computational complexity. Compared with state-of-the-art recursive algorithms, the effectiveness of the proposed method is shown by studying on a numerical case and a practical industrial system.

Prediksi Spot Price Komoditas Emas Berjangka dengan Pendekatan Vector Error Correction Model

Time series data usually exhibit non-stationary behavior and involve interrelated variables. Thus, we need a model that can obtain good forecasting results from non-stationary time series data with multivariate variables. The Vector Error Correction Model (VECM) is a multivariate time series model which is a vector form of Vector Autoregressive Regression (VAR) for time series data that are non-stationary and have a cointegration relationship. This research was conducted to model the cointegration relationship in providing clarity on the long-term relationship of the influence of future prices and the Covid-19 pandemic on price movements of gold futures commodities and to predict spot price prediction modeling for gold futures commodities. The results of the research using the VECM (2) model, which is the best model, show that the future price of the gold commodity is quite dominant in influencing the value of the spot price of gold. The Covid-19 variable does not have a significant effect on the spot gold price variable.

Exploring the causal relationships between health, pollution, and growth: Evidence from OECD countries

This study examines the impact of particulate matter and income levels on three mortality indicators in a sample of OECD countries from 1990 to 2017. The em-pirical analysis is performed within a panel cointegration framework to test for cointegration and causal relationships. The findings support an equilibrium rela-tionship holding mortality rates with pollution and income in the long-run. Esti-mated coefficients and causality tests show that increasing pollution rises mortali-ty rates while increasing GDP per capita has the opposite effect. The main contri-bution of this study is to bring attention to the relationship between mortality rates, fine particulate matter and income for a panel of OECD countries combining coin-tegration analysis and causality tests.

What is the global causality among renewable energy consumption, financial development, and public health? New perspective of mineral energy substitution

Under the background of global warming and economic globalization, energy structure transformation and financial liberalization have become hot issues of global concern. Additionally, the replacement of renewable energy sources for mineral energy is placed great expectations to reduce environmental pollution and promote public health. Although the effects of renewable energy consumption (REC) and financial development (FD) on public health (PH) have been verified by researchers, the long-term causality among the three has not been explored, especially from global evidence. To verify the long-term relationship and causality among REC, FD, and PH at the global level, using annual panel data for 136 nations from 2000 to 2018, this paper employs the Cross-sectional dependence test, CADF unit root test, the second generation cointegration test, Pooled Mean Group estimation, and the Granger causality test. This paper finds some interesting results. First, the long-term co-integration relationship and the two-way causal relationship are among REC, FD, and PH. Second, REC and FD have a long-term promoting effect on PH, but have no significant effect in the short term. This conclusion holds up after a series of robustness tests. Third, there is heterogeneity in the long-term impact of REC and FD on PH. The long-term PH benefits of REC are more vital in low-polluting countries, trade-open countries, poor-institution countries, and non-emerging market countries, while FD has a long-term negative impact on PH in high-polluting and trade-tight countries. Based on the above results, we believe that it is necessary to formulate appropriate energy policies and financial policies according to local conditions. In summary, our results provide essential policy recommendations for energy transformation and public health improvement in countries around the world in the process of financial liberalization.

Döviz kuru oynaklığının ithalat üzerindeki etkisi: Yükselen piyasalardan kanıtlar

This study investigates the co-integration relationships among the import, exchange rate volatility, income, and real exchange rate for the emerging market economies during 2000 - 2020. Apart from the previous studies, we consider the cross-section dependency in emerging markets. For the co - integration analysis, Westerlund and Edgerton (2007) co-integration test was used to obtain effective results by allowing autocorrelation and varying variance in the co - integration equation. The findings of this study show that there is a co -integration relationship between the variables. The impact of volatility, income, and real exchange rate on imports are estimated using common correlated effects (CCE). The findings of the panel indicate that long-run imports are significantly impacted negatively whenever there is volatility in emerging markets. It is seen that tastes and preferences are directed towards imported goods, and an increase in income leads to an increase in imports. At the same time findings contradict theoretical projection and shows that an increase in the value of the currency exchange rate has a positive influence on the quantity of goods imported. This could be due to the fact that exports revenue in emerging market countries depend on imports.

Effects of Some Macro-Economic Variables on Agricultural Output: Implication for Food security in Nigeria (1980-2020)

The study explored the effects of some macroeconomic variables on agricultural sub-sector output: implication for food security in Nigeria (1981-2020). The study specifically looked at the effects of money supply, interest rate, inflation rate, exchange rate and government recurrent expenditure on agricultural output in Nigeria. Central Bank of Nigeria (CBN) Statistical Bulletin, index mundi and journals were used as sources for the annual time series data on the research variables for the period 1981–2019. The Autoregressive Distributed Lagged Model was used in the study. The unit root test revealed in the pre-diagnostic tests that the variables were stationary at 1(0) and 1(1). The ARDL Bound test for co-integration revealed that the variables had long-run co-integrating relationship. Additionally, empirical findings indicated that, in the short run, exchange rate, government recurrent expenditure and money supply as macroeconomic variables affected agricultural output The study recommends that since the research showed that money supply had positive effect on agricultural output, government should inprove on money supply so as to enable farmers meet up their required purchases.

The Dynamic Simulation Analysis of the Impact of Urbanization and Globalization on Environmental Quality

This study aims to analyze the effect of urban population and economic globalization on air quality in Turkey for the period 1970–2017, including GDP and electricity consumption as control variables. This paper is the first attempt to apply the dynamic autoregressive-distributed lag (DARDL) approach to explore the association between carbon emissions, urbanization, economic globalization, GDP, and electricity consumption in Turkey. The analysis results suggested the existence of a cointegration relationship between all series in the long run. DARDL results revealed that while urbanization has a statistically significant effect on carbon emissions in the short or long run, economic globalization has a positive impact in the long run. That is, economic globalization increases carbon emissions by about 0.15 percent. Thus, it can be said that globalization is more critical than urbanization in terms of environmental costs. In addition, it was determined that GDP and electricity consumption increase carbon emissions in both the long and short run. Also, the response of CO2 emissions to all shocks in the explanatories was determined. All future shocks of electricity consumption lead to no change in CO2 emissions. The negative shocks of both urbanization and globalization decrease CO2 emissions in the short run, but the response of CO2 is stable in the long run. The effects of a shock in GDP are exactly the opposite.

Testing of Constant Parameters for Semi‐Parametric Functional Coefficient Models with Integrated Covariates

Cointegration has been widely used in macroeconomics and financial time series analysis, but traditional linear cointegration relationship is often rejected in empirical applications. Many constant parameters testing methods in semi‐parametric functional coefficient cointegrated framework have been developed accordingly. However, there are little studies on constant parameters testing problem for the case that the index variable is integrated of order one. From a practical point of view, there is also a need for a test that accommodates integrated index variable in functional coefficient cointegrated setting, for example, in the study of the purchasing power parity hypothesis. In this article, an orthogonal series approximation‐based test statistic is proposed to tackle the problem. The asymptotic results are also studied. Monte Carlo experiments are conducted to evaluate the finite sample performance of the proposed test, and an empirical example about price and exchange rate data is provided.

Energy imports as inhibitor of economic growth: The role of impact of renewable and non-renewable energy consumption

ABSTRACT This study investigates the role of energy imports in domestic production function in the case of 15 energy-importing countries for the period of 1995–2015. Apart from energy imports, renewable energy consumption, non-renewable energy consumption, capital, trade openness, and urbanization are included in the growth model. In doing so, long-run elasticity coefficients are estimated with the Dynamic Seemingly Unrelated Regressions (DSUR) model after determining the cointegration relationship between the variables. In addition, for robustness checks, Panel Correlated Standard Errors (PCSE) and Feasible Generalized Least Squares (FGLS) estimators are applied. Our results show that renewable energy consumption, non-renewable energy consumption, trade openness, and capital have a positive effect on economic growth. Energy imports negatively affect economic growth. The Dumitrescu-Hurlin causality analysis reveals a bidirectional causality relationship between renewable energy consumption, capital, trade openness, urbanization, and economic growth. A unidirectional causality relationship exists from economic growth to non-renewable energy consumption and energy imports. This analysis can be interpreted as having a negative impact on economic growth by putting pressure on the current account deficit due to energy imports. Therefore, investments in the renewable energy sector will play an important role in economic growth.

Does Urbanization Matter For Poverty Reduction in Nigeria: An Empirical Evidence From Autoregressive Distributed Lag (ARDL) Estimation

Abstract Urbanization has been argued to be having an impact on several other development challenges. To this end, this paper aims to contribute to the empirical literature by exploring the effect of urbanization and its' magnitude on poverty, both in the short run and long run in Nigeria. The macroeconomic analysis was conducted using data from 1982 to 2017 which was obtained from the World Bank. Bound Test and autoregressive distributed-lag (ARDL) estimation techniques were used to test the existence of a cointegration relationship and to estimate the short and long-run effect of urbanization and other variables on poverty reduction. Results from the study and an economic standpoint, provide strong evidence that urbanization remains an important factor in poverty reduction in Nigeria. The analysis further shows that while international remittances have a positive and significant effect, foreign aid and government expenditure have significant negative effects on poverty reduction in the long-term period. While findings from this study suggest that urbanization remains a valid tool in the fight against poverty, the need for sustainable urbanization policies and efforts by the Nigerian government is highly imperative.

A dynamic relationship between renewable energy, agriculture, globalization, and ecological footprint of the five most populous countries in Asia.

This study aims to examine the impact of globalization, renewable energy consumption, and agricultural value addition on the ecological footprint of selected five most populous countries in Asia during the period 1975-2020. The Westerlund cointegration test supports long-term cointegration relationships among the considered variables in selected countries. The long-term resilience results of the second-generation cross-sectionally augmented autoregressive distributed lag approach evidently demonstrate that agricultural value addition and globalization contribute significantly to the long-term ecological footprint of the five most populous countries in Asia. However, renewable energy consumption significantly reduces the ecological footprint. Moreover, the impact of economic growth on ecological footprint is significantly positive, while the square of economic growth had a significantly negative impact on ecological footprint, thus validating the inverted U-shaped environmental Kuznets curve hypothesis for specific Asian densely populated countries. The causality test results of Dumitrescu and Hurlin support the feedback hypothesis by showing a two-way causal relationship between renewable energy consumption and economic growth. There is also a two-way causal relationship between agricultural value added and ecological footprint. Strategically, specific densely populated countries in Asia should encourage clean energy production and consumption in the agricultural sector, and the adoption of environmentally friendly technologies can improve environmental quality and agricultural production.

MEMPERCEPAT PEMULIHAN EKONOMI, MENJAGA STABILITAS NILAI TUKAR

VAR models for non-stationary time series with one or more cointegration relationships have been developed using the Vector Error Correction Model (VECM). The unique way of behaving in the VECM should be seen through the reactions of each dependent variable to shocks to these and other environmental factors. The purpose of this study is to determine the relationship between Indonesia's economic expansion and exchange rate stability. With a free-floating regime from 1980 to 2022, Indonesia's exchange rate fluctuated, affecting economic stability. Exchange rates are correlated with economic expansion in both the short and long term, according to this study. Long-term economic growth can be driven by exchange rate appreciation, but there is no causal relationship because economic growth factors go beyond exchange rate considerations