Cointegration Relationship Research Articles

THE NEXUS BETWEEN CHILD ABUSE AND ECONOMIC CONDITIONS: EMPIRICAL EVIDENCE FROM MALAYSIA

This research attempts to examine the impact of economic conditions on number of child abuse cases in Malaysia. Yearly time series data has been included in data collection from year 1988 until 2019. To achieve the objective of the study, an estimation method of Autoregressive Distributed Lagged (ARDL) has been employed. The ARDL methodology consists of two steps analysis; first, ARDL Bound test which to determine the existence of cointegration relationship between child abuse and economics condition and second, ARDL Level Relation test with purpose to identify the cointegration relationship does significant in the long run. The findings have found cointegration relationship among the variables. However, only inflation (INF), unemployment (UEM), and minimum wage (MIN) appear significant to affect number of child abuse cases in Malaysia in the long run. The results suggest except poverty (POV), the number of child abuse cases in Malaysia are fully adjusted by the movement in INF, UEM and MIN. Overall, output from this study provide recommendation for policy-makers in readjusting the existing policies so that they able to curb the rising number of child abuse as well as to promotes economic growth and peaceful environment in Malaysia.

Türkiye’de Mali Sürdürülebilirliğin Analizi Bootstrap Fourier ARDL Yaklaşımı

Fiscal sustainability is one of the most important indicators of whether the public budget is within fiscal discipline. The sustainability of the public budget within the discipline affects many macroeconomic variables, such as inflation, economic growth, and income distribution. In this study, fiscal sustainability, one of the important determinants of macroeconomic factors, has been analysed by the Bootstrap Fourier ARDL cointegration method for the period 2006:01-2021:12 in Türkiye. A long-run cointegration relationship has been found between the variables. After determining the cointegration relationship, the long-run coefficient, short-run coefficient, and error correction model coefficient have been calculated. At a 5% significance level, the long-run coefficient was 0.36, the short-run coefficient was 0.21 and the error correction model coefficient was -0.54. As a result of the empirical findings, fiscal sustainability in Türkiye has been found to be weak in the relevant period.

OIL PRICES SHOCKS AND INFLATION RATE PERSISTENCE FOR ALGERIA: A FRACTIONAL COINTEGRATION RELATIONSHIP

This study aims to analyse the relationship between oil prices and inflation rates in Algeria to determine the extent of inflation persistence in face to oil price shocks from January1998 to March 2023 using the recently developed Fractional Cointegration Model, which allows residuals to be fractionally integrated rather than stationary, with the classical cointegration approach based on I(0) stationarity or I(1) cointegrating relationships. This topic was chosen due to its significance for monetary policymakers, investors, financial analysts and academics in understanding the dynamics of inflation persistence in Algeria and studying the impact of some price shocks on it, such as oil price shocks. Our results also showed that Algeria has a co-integration relationship between oil prices and the inflation rate, with an estimated persistence of 0.883, which is greater than 0.5 and less than 1. This indicates that the impact of oil price shocks is still present for a long time on the inflation rate persistence in Algeria, in other words the inflation rate in Algeria will persistence for a longer period due to the shock of oil prices before eventually fading away, and with the adoption of a monetary policy targeting inflation in Algeria, it will contribute to reducing the inflation rates persistence.

Dynamic Effects of Energy Consumption and Economic Growth on CO2 Emission: Testing EKC Hypothesis in Africa

This paper focuses on using time series data on real GDP, energy consumption, and CO2 emission to examine the effect of economic growth and energy consumption on CO2 emission for a panel of 23 African countries within the period 1980–2019. The study used Pedroni (1999) approach of panel cointegration analysis to test for existence of long-run cointegration relationship between the variables. Fixed effect model was used to test for the Environmental Kutznets Hypothesis, and income squared was included as an additional explanatory variable. The estimated empirical results for the panel of 23 African countries from fixed effect model indicates the evidence of EKC hypothesis. At the level of individual countries, there is large divergence. 13 countries show evidence of EKC, implying that CO2 emission has fallen over the long run. As income increases, the levels of environmental damage decreases in those countries. 10 countries show opposite relationship among the variables. Based on the estimated results, it is recommended that countries should pursue economic growth policies that are not highly carbon intensive. Policy makers in these countries should adopt strategies that uses environmentally friendly technologies to decrease CO2 emission. Countries should also implement strong regulatory and market-based policies in highly energy-intensive sectors to reduce their current level of emissions and attain sustainable, environment-friendly economic growth.

The contribution of green finance to energy security in the construction of new energy system: Empirical research from China

Previous studies have discussed theoretically the role of green finance in energy security, but empirical studies have yet to explore their relationship. In order to make up for this deficiency, we first measured the development level of green finance and the comprehensive score of energy security in China from 2000 to 2020. Then, we constructed a vector error correction model based on the cointegration relationship. The results show a stable cointegration relationship between the long-term variables, and the improvement of the development level of green finance is conducive to ensuring China's energy security. However, from the results of the short-term dynamic adjustment effect, the change in green finance development level has no significant impact on the change in energy security level because the role of green finance has a specific time lag. Further, through impulse response analysis, it is found that when the development level of green finance has a unit positive impact, energy security will produce a first negative and then positive response. The variance decomposition results show that the contribution of green finance to energy security first weakens and then gradually increases. To sum up, the long-term development of green finance will help stabilize China's energy security. Therefore, this study proposes strengthening supervision over green finance, developing “new energy + green finance”, achieving positive interaction between green finance and transformational finance, and enabling green finance with digital technology.

The Effect of Foreign Direct Investment and Economic Development on Renewable Energy in Indonesia

The study aims to explore the indicators of foreign direct investment (FDI) and their impact on economic development, with a specific focus on renewable energy in Indonesia. It is well-known that FDI plays a significant role in the development of developing countries, and Indonesia, being one of them, still faces challenges in meeting the energy needs of its citizens. The empirical analysis utilizes the Multiple Regression approach with data spanning from 1981 to 2021. The results indicate a cointegration relationship between model parameters and cross-sectional dependence. Additionally, the study finds that FDI and inflation have a negative impact on renewable energy, while population and GDP have a positive and significant effect. Furthermore, economic growth and fossil fuel consumption also positively influence renewable energy consumption. In the long term, the estimation results suggest that FDI and financial development have a simultaneous effect on energy consumption, which aligns with economic growth but not necessarily with renewable energy consumption. This implies that while FDI and financial development contribute to overall energy consumption as the economy grows, they may not directly impact the adoption of renewable energy sources. Based on the research findings, policymakers in Indonesia are encouraged to focus on sustainable development and consider policy transformations that facilitate the transition from fossil fuels to renewable energy sources.
 Keywords: FDI, GDP, inflation, population, renewable energy, multiple regression

Yabancı turistlerin kullandığı ulaşım türleri ile ekonomik büyüme arasındaki dinamik ilişki: Türkiye Cumhuriyeti'nin son 50 yılından kanıtlarla ekonometrik bir analiz

This article aims to reveal the relationship between the Turkish economy and tourism, with a retrospective evaluation of the 100th anniversary of the Republic. In attempting to determine this relationship, the question is asked whether the tourism-led growth (TLG) hypothesis or the economy-led growth hypothesis through the expansion of tourism is valid based on the last 50 years of the republican period. The ARDL approach (Autoregressive Distributed Lag) investigates the interaction between variables. A cointegration relationship was found between the variables and a positive relationship between the tourists arriving by air and sea and economic growth in the long run. The Granger causality test was applied to determine the presence and direction of causality between tourists arriving in Turkey and GDP. The results show that economic growth is the Granger causality for tourist arrivals. Relating these results to the variables studied and the study period for the Turkish economy confirms the validity of the hypothesis of tourism expansion based on economic growth rather than the hypothesis of tourism-induced growth.

Analyzing asymmetric ecological performance under structural change, technological innovation, and trade diversification: fresh insights from the USA.

The present research scrutinizes the influences of trade diversification, air transportation, technological innovation, and economic complexity on ecological footprint from 1990 to 2019. The findings of the both time series unit root (with and without structural break) tests confirm that none of a single variable is stationary more than the first difference. Furthermore, the Wald and nonlinear autoregressive distributed lag bound methods check asymmetry and long-term cointegration relationship between the intended variables, respectively. Moreover, this study uses the nonlinear autoregressive distributed lag model to estimate the short-run and long-run coefficients/elasticity of the ecological footprint function. Following the empirical evidence, the findings revealed that positive (negative) components in trade diversification curtail the ecological footprint in the long-run. In addition, a positive shock in air transportation leads to an increase in ecological footprint in the long-run. Nevertheless, a negative shock in air transportation exerts a significant and adverse influence on the level of ecological footprint in the long-run. Furthermore, a positive (negative) shock in technological developments significantly reduces environmental pollution in the US economy in the long-run. Besides, the outcomes from economic complexity discovered a positive shock will significantly overcome the pressure on the environment in the long-run. However, in the short-run, it is observed that negative shock in trade diversification will lead to increase the ecological footprint level in USA. Similarly, a positive shock in air transportation will lead to increase the pollution level in the short-run. In contrast, a negative shock in air transportation will lead to reduce the pressure on the environment in the short-run. Besides, in terms of policy realization, the present research recommends adopting trade synchronization, harmonic trade strategies, and investment in technological innovations to diminish the existing level of ecological footprint in the region. For sustainable development, this study put forward for instantaneously encouraging the expansion of the digital economy and reducing air pollution, accelerating the green transformation, and impelling the industrial agglomeration process in the USA.

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.

Exploring the effects of energy consumption on millets and rice yields in Odisha, India

Energy is a crucial input in determining the agricultural growth of an economy. Thus, this study aims to investigate the impact of energy consumption (EC) on agricultural productivity growth (APG), specifically in relation to rice and millet yields in Odisha, India. The study uses data from the period spanning 1990–91 to 2019–20. The study uses an autoregressive distributed lag (ARDL) bound F-test to examine for a cointegration relationship amid the variables, and an ARDL regression framework to investigate the effect of EC on the yields of rice and millets in Odisha. The results advocate that there exists a long-run equilibrium relationship amid the variables in the study. Regarding the result for long- and short-run elasticities, it is evidenced that EC has a positive and statistically significant effect on rice yield and has no statistically significant impact on millet yield in the long-run. However, in the short-run, EC has a positive and statistically significant effect on both rice and millet yields. According to the findings of the current study, the state government of Odisha should place a greater emphasis on ensuring that farmers have access to power in order to encourage agricultural expansion and improve crop yields.

Natural Disasters, Economic Growth, and Carbon Emissions: Empirical Analysis of Chinese Data Based on a Nonlinear Auto-Regressive Distributed Lag Model

China has a high frequency of natural disasters and it has become the economy with the largest carbon emissions in recent years. In this study, we mainly investigated the relationships between carbon emissions and natural disaster losses in China, as well as considering important factors such as economic growth and new energy consumption. Time series data for China from 2000 to 2020 were selected and based on the nonlinear auto-regressive distributed lag model method, a short-term error correction model and long-term co-integration relationship model were obtained between carbon emissions and their related factors. The results showed that in the long run, there is a significant nonlinear relationship between carbon emissions, new energy consumption and direct economic losses from natural disasters. There is a significant U-shaped relationship between natural disasters and carbon emissions, that is, natural disaster losses will significantly inhibit carbon emissions before they are below a certain threshold, but fewer natural disaster losses will increase carbon emissions. On the contrary, there is an inverted U-shaped relationship between new energy consumption and carbon emissions. When new energy consumption exceeds a certain threshold, it will help carbon peak early. In the short term, the impact of natural disasters on carbon emissions in the current period is significantly positive and higher than that in the lagged period, but the impact of its square term is negative. The short-term error correction model coefficient is −0.6467, and the error will be corrected when the short-term volatility deviates from the long-term equilibrium. These results suggest that attention should be paid to reducing disaster losses and the low-carbon reconstruction path for natural disasters, as well as continuously improving the level of new energy utilization, accelerating the pace of energy substitution, and promoting economic transformation for achieving “carbon peaking” in China.

Comparison of the Asymmetric Relationship between Bitcoin and Gold, Crude Oil, and the U.S. Dollar before and after the COVID-19 Outbreak

This paper aims to reveal the asymmetric co-integration relationship and asymmetric causality between Bitcoin and global financial assets, namely gold, crude oil and the US dollar, and make a comparison for their asymmetric relationship before and after the COVID-19 outbreak. Empirical results show that there is no linear co-integration relationship between Bitcoin and global financial assets, but there are nonlinear co-integration relationships. There is an asymmetric co-integration relationship between the rise in Bitcoin prices and the decline in the US Dollar Index (USDX), and there is a nonlinear co-integration relationship between the decline of Bitcoin and the rise and decline in the prices of the three financial assets. To be specific, there is a Granger causality between Bitcoin and crude oil, but not between Bitcoin and gold/US dollar. Before the outbreak of the COVID-19 pandemic, there was an Asymmetric Granger causality between the decline in gold prices and the rise in Bitcoin prices. After the outbreak of the pandemic, there is an asymmetric Granger causality between the decline in crude oil prices and the decline in Bitcoin prices. The COVID-19 epidemic has led to changes in the causality between Bitcoin and global financial assets. However, there is not a linear Granger causality between the US dollar and Bitcoin. Last, the practical implications of the findings are discussed here.

The Determinants of Tax Revenue: The Case of Turkey

Tax is the most basic public financing instrument of the treasury. Tax is an important instrument of fiscal policy as well as its role in financing public investment and public services. For these reasons, it is imperative for all countries must achieve a stable level of tax that allows public needs to be met. Economic and financial factors affecting the performance of tax revenue play an important role in the determination and implementation of tax policies. This study investigated the determinants of tax revenues in Turkey. For this purpose, the effect of USD/TL exchange rate, M2 money supply, industrial production index, deposit interest rate, unemployment rate and export variables, which are considered to be effective on the tax level, on the tax level in Turkey for the period 2006M1- 2022M11 within monthly observations, is analyzed by ARDL methods. The empirical findings reveal that there is a cointegration relationship between tax revenues and all series. According to the long-run coefficient estimates, M2 money supply, industrial production index and deposit interest rate affect the tax level positively, while unemployment rate and USD/TL exchange rate affect the tax level negatively.

Impact of financial development, energy consumption and urbanization on CO2 emissions from buildings using quantile ARDL model

Urbanization plays a crucial role in facilitating the integration of population growth, industrial development, economic expansion, and energy consumption. In this paper, we aim to examine the relationships between CO2 emissions and various factors including economic growth, urbanization, financial development, and energy consumption within Pakistan’s building sector. The study utilizes annual data spanning from 1990 to 2020. To analyze the cointegration relationship between these variables, we employ the quantile autoregressive distributed lag error correction model (QARDL-ECM). The findings of this research provide evidence supporting the presence of an asymmetric and nonlinear long-term relationship between the variables under investigation. Based on these results, we suggest the implementation of tariffs on nonrenewable energy sources and the formulation of policies that promote sustainable energy practices. By doing so, policymakers and architects can effectively contribute to minimising environmental damage. Overall, this study offers valuable insights that can assist policymakers and architects in making informed decisions to mitigate environmental harm while fostering sustainable development.

Türkiye Ekonomisinde Bankacılık Sektörü Tarafından Yaratılan Yurt İçi Kredi Hacmi İle Ekonomik Büyüme Arasındaki İlişki: ARDL Sınır Testi Yaklaşımı

Banks are one of the most important elements of the financial system. Banks are organizations that are involved in the development of the money market by ensuring the collection of savings in the economy, transferring surplus funds to economic units that need funds. Banks expand the money supply through the credit channel and ensure that savings are converted into investments in the economy. Increasing investments in the country will contribute to production and employment. The rise in the number of goods and services produced will also lead to development in the rate of economic growth. From this point of view, this study analyzes the short and long-run effects of the aggregate domestic credit volume created by deposit banks, development and investment banks, and participation banks on economic growth in the Turkish economy by adopting the autoregressive distributed lag bounds testing (ARDL) approach for quarterly data covering 2008:01-2022:02 periods. As a result of the analysis, a cointegration relationship has been found between the aggregate domestic credit volume and economic growth. It is concluded that there is no long-term relationship between the aggregate domestic credit volume and economic growth, but they are in a relationship in the short term.

Relationship between Technology and Economic Growth

The study analyzes the relationship between technology and economic growth in Jordan during 2009–2018 and the data are treated via Views E program. ARDL methodology are used. Results showed a co-integration relationship between the study variables (computer use in general; computer use at work; and computer use in education, training, and economic growth) and the results presented that the deviation from long-term equilibrium is corrected using an error correction model which long-term corrected as a percentage correction (−0.06) each year from the short-term to the long-term and showed the results of the structural stability test of the (ARDL) model. It is a structural stability test for long and short-term coefficients, which showed that the data used in this study are free from any structural changes has stable parameters over time The study also used CUSUM's Squerse test, where the test results showed that the study model used is economically good and can be relied upon to anticipate economic solutions in Jordan according to the situation in the coming years, and among the most important recommendations of the study are the following: the need to encourage the use of technology in work, education, and training, and the need for expansion in using these technological means as a gateway to the digital economy and the digital state.

Economic growth factors of industrial and commercial enterprises in coastal cities based on the model of unexpected super efficiency

In order to improve the economic growth efficiency of industrial and commercial enterprises in coastal cities and realize the GDP growth of coastal cities, this paper studies the economic growth factors of industrial and commercial enterprises in coastal cities based on the unexpected super efficiency model. Based on the research and analysis of the previous economic growth theories, this paper finds out the main factors that affect the economic growth of industrial and commercial enterprises in coastal cities, and uses the advanced econometric method to establish the relevant test model to analyze the correlation between the time series of economic growth factors and the time series of coastal cities, so as to realize the economic growth factors of industrial and commercial enterprises in coastal cities Element study. The empirical results show that the main factors affecting the economic growth of industrial and commercial enterprises in coastal cities are capital and labor force, with labor force as the main body; Technical and institutional factors also contribute to the GDP of industrial and commercial enterprises in coastal cities, but the impact is not significant and needs further improvement. In general, these factors can promote the economic growth of industrial and commercial enterprises in coastal cities. The time series and time series of each factor variable are first-order non-stationary series with long-term cointegration relationship.

Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China

In the structure of energy consumption of China, coal and oil rank the top and the second place. It is obviously important to better understand the dynamics of the price volatility between crude oil and coal in China, and then to build a reasonable energy portfolio. As the listing of China's crude oil futures offers a new sample to study the volatility contagion of oil and coal prices, by using daily data from 2nd May 2018 to January 23, 2020, four different multivariate GARCH models are compared and contrasted in this paper. Then, based on the results of the four MGARCHs, we investigate the oil-coal portfolios' optimal weights and hedge ratios. The results indicate that, first, a long-run co-integration relationship exists in China's crude oil and coal. Second, in short run, futures price of crude oil leads that of coal, the volatility spillover effect of crude oil on coal is stronger than that of coal on crude oil. Third, the DCC-GARCH model fits best for data. Last, weights of optimal portfolio and ratios of hedge show that the crude oil is a tool to make the risk of portfolio minimal in energy markets. These results are expected to have implications for energy producers, users and trading enterprises to inform their decision-making.

Industrialization, globalization, ICT, and environmental degradation in Malaysia: A frequency domain analysis

This paper examines the causal relationship between industrialization, globalization, information communication technology (ICT) and environmental degradation in Malaysia during 1970–2019. It uses two indicators of environmental degradation (carbon emissions and ecological footprint), three dimensions of globalization (political, social, and economic) and three indicators of ICT (users of internet, mobile cellular, and fixed telephone subscriptions). It utilizes Granger causality technique in frequency domain which differentiates between permanent and temporary causality, Vector Error Correction approach as well as Variance Decompositions. The bound test shows that the variables have cointegration relationship. It reveals joint long-run and short-run causality from industrialization, globalization, and ICT to carbon emissions, albeit the causality to ecological footprint is tenuous. It indicates that industrialization, globalization, and ICT significantly predict carbon emissions at high frequency than at low frequency. A substantial percentage of the forecast error variance in environmental degradation are explained by industrialization, globalization, and ICT. The robustness of the empirical outcomes is confirmed by the alternative proxies of the variables. Our study implies that industrialization, globalization, and ICT are determinants of environmental degradation. Therefore, policies to mitigate environmental problem should prioritize these variables to attain green economy.

Nexus Among Digital Economy, Green Innovation, and Green Development: Evidence from China

ABSTRACT Green development is an essential requirement for the high-quality development. In the context of the new round of technological revolution, the digital economy has injected new momentum into China’s high-quality economic development. This article aims to clarify the relationships among digital economy, green innovation, and green development using Westerlund and Edgerton (2007) cointegration test as well as pooled mean group (PMG) estimation and utilizing yearly data from 274 cities in China from 2011 to 2019. Overall, the results confirm the existence of cointegration relationships between green development and green innovation, between green development and digital economy, as well as between digital economy and green innovation, and support that digital economy and green innovation can affect the green development in the long-term positively. Additionally, while digital economy positively affects the green development in the short-run, green innovation cannot affect the green development. Furthermore, while the long-run positive impact of digital economy and green innovation on green development is generally established among eastern, central, and western regions, the short-run impact of such two factors on green innovation varies among different regions, seeing as both two variables exert no significant impact on green development in western region. Empirical findings offer important policy implications for the policymakers to attach more importance on the emerging economy model such as digital economy or green innovation, which can bring about long-term green development.