Cointegration Relationship Research Articles

Unlocking Urbanization: The symbiotic relationship between inclusive finance and urban development in China

The emergence and development of inclusive finance has made it possible for more economic entities to have easy access to a wider selection of financial services. This shift has significantly addressed the financial challenges inherent in the process of urbanization, making it a driver of the process of urban development. Therefore, this paper provides empirical evidence on the relationship between financial inclusion development and urbanization construction in China using provincial data and a panel-VAR model. The results show that: (1) There is a significant co-integration relationship among inclusive finance, urbanization, government support, and real estate development. (2) Inclusive finance has a long-term positive impact effect on urbanization. (3) Population urbanization has a positive impact on inclusive finance, but income urbanization has a negative impact on inclusive finance. To effectively promote the development of inclusive finance and urbanization, the following measures are of utmost importance: Firstly, while accelerating urbanization construction, it is necessary to expand and enhance the coverage of financial services. This will ensure that multiple regions can benefit from financial services. Secondly, to meet the diverse needs of different regions, more targeted financial products should be developed, making full use of the advantages of inclusive finance. Lastly, the government should strengthen its supervision of financial institutions and reduce the risks associated with inclusive finance, thereby ensuring a positive interaction between inclusive finance and urbanization development.

Fiscal Policy and Economic Growth a Cointegration Approach Based on Structural Breaks: Evidence from Algeria

In this paper, we studied the impact of fiscal policy tools on economic growth in Algeria for the period from 1970 to 2022, and after testing this impact, we took that oil taxes and non-oil taxes represent two independent variables of economic growth. This study shows that there is a long-term relationship between the two variables. The study also found that the cointegration test of Gregory Hansen and Hatami -J applies under regime change. The results of the study showed that there is a cointegration relationship between the variables in the case of the presence of one constructive interval. As for the two structural breaks, our paper confirmed the presence of a cointegration The relationship between these variables. We find the long-run coefficients for. The cointegration test has showed that there is an absence of a cointegration relationship, This method supported the hypothesis of the absence in a cointegration relationship in the case of the absence of structural breaks, while when we used the cointegration approach based on breaks, we confiremed clear evidence of this, in the case of more than three strictual breaks in the long-run relationship between variables.The results of this study were confirmed by the Maki test, for example it appears first that fiscal policy exerts a weak positive impact on economic growth, after 2015, while regular taxes are only affected in the long run according to New Keynesian expectations, and it is natural that taxes exert the opposite effect. We concluded that this is due to the decline in the price of oil and the impact on its revenues for approximately 5 years

아파트 가격의 파급효과 분석 - VECM을 중심으로

This paper investigates the spillover effects of apartment prices. The transaction price index was used to estimate the connectedness among the five major regions of Seoul and among Seoul and six major metropolitan cities. In particular, considering the cointegration relationship between housing price indices, we apply the VECM model and estimate the connectedness by focusing on the southeast region of Seoul, which is generally accepted to play an important role in the domestic housing market. The sample period is from January 2006 to September 2022. The main findings are as follows. First, a cointegration relationship exists among regions in Seoul and among Seoul and the six metropolitan cities. In particular, we found that the spillover effects among housing markets are underestimated when using the first differenced-VAR model instead of VECM in situations where cointegration exists. Second, the southeast region of Seoul has been the most influential market in Korea. Third, the rolling sample analysis revealed increased interdependence among housing markets after the outbreak of the COVID-19 pandemic.

ANALYSIS OF COINTEGRATION AND CAUSALITY BETWEEN INDICATORS OF ECONOMIC GROWTH AND ENERGY EFFICIENCY OF EUROPEAN COUNTRIES

In modern conditions, when European countries have set themselves an extremely ambitious goal of reducing greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels, it is important to analyze the cause-and-effect relationships between key indicators of energy efficiency of national economies and economic growth, the nature of their influence on each other. The article analyzes cointegration and causal relationships between panel data that determine the economic development and energy efficiency of 38 European countries for the period from 1995 to 2021. Stationary time series were analyzed for causality using the Dumitrescu Hurlin test, which, compared to the classical Granger test, more accurately takes into account the structure of panel data, namely cross-sectional relationships. The annual GDP growth rate has driven the intensity of CO2 emissions. For pairs of time series with the first level of integration, in the case of cointegration between them, a Vector Error Correction Model (VECM) was used to determine the type of long-term behaviour of the variables with their short-term feedback. Long-term causality was found from GDP per capita to the level of primary energy intensity of European countries. Exports of goods and services have proven to be a long-term cause of domestic consumption of natural gas and solid fossil fuels. Bidirectional long-term causality was found only between primary energy consumption and exports. It should be noted that in all short-term and long-term cause-and-effect relationships obtained in the article, economic development indicators are the cause for energy efficiency indicators. This signals that the level of energy efficiency of the European economy is determined to a large extent by the economic development of Europe in previous periods. ARDL models can be used to analyze causal relationships between time series that have different levels of integration.

Assessment of the Impact of Financial Intermediation on Gross Domestic Product in Nigeria from 1996 to 2022

This paper assesses financial intermediation in Nigeria and how it has impacted economic growth. The study sourced time series data between 1996 and 2022 from the Central Bank of Nigeria and utilized econometric techniques using Ordinary least square regression, Error correction model, and Pairwise Causality to assess the impact of the financial intermediation institutions on the growth of the nation's Gross Domestic Product. The paper focused on banks, capital markets, and the insurance industry being significant institutions in the financial system. The GDP is the dependent variable, while Credit to the Private Sector as a percentage of GDP (CPS/GDP) and Total Savings as a percentage of GDP (TS/GDP) were used as proxies for assessing banks' contributions to economic growth. Stock market capitalization was used as a proxy for the contribution of the capital market to economic growth, and Insurance industry assets were used as a proxy for the insurance industry's contribution to economic growth. The study revealed no co-integration and long-term relationship between economic growth and the financial intermediation variables. There was, however, a unidirectional causal relationship between economic growth and the banks and the capital markets. There was no causality between economic growth and insurance. We recommend that policymakers undertake periodic reforms to deepen the capacity of the institutions to support de-risking and funding small and medium enterprises and the agricultural value chain, which are vital channels for diversifying the economy. The regulatory authorities are pivotal in upscaling the institutions' contribution to economic development.

ESTIMATING THE RELATIONSHIP BETWEEN TECHNOLOGY AND ECONOMIC GROWTH - EVIDENCE FROM SIERRA LEONE EXPERIENCE

<p>The study analyzes the relationship between technology and economic growth in Sierra Leone from 2011 to 2022 and the data are treated via the E-Views program. ARDL methodology was used. The 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 but has stable parameters over time. The study also used CUSUM's Squares test, where the test results showed that the study model used is economically good and can be relied upon to anticipate economic solutions in Sierra Leone 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.</p><p><strong> </strong></p><p><strong>JEL</strong>: O33, O47, C51, C22, O14, O55</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0708/a.php" alt="Hit counter" /></p>

The Role of the National Gas Policy on the Nigerian Electricity Supply Industry and Economic Growth

This study examined the relationship between real GDP, electricity supply, and the National Gas Policy (NGP) in Nigeria, using annual time series data from 1981 to 2019. To achieve its aim, the study explores how the NGP might influence future economic growth, natural gas reserves and electricity supply. Econometric techniques are employed for analysis, where real GDP is dependent variable, and the NGP is the dummy explanatory variable alongside other explanatory variables. The Augmented Dickey-Fuller and Phillip Perron Methods were used for unit root tests. The ARDL bounds tests indicated evidence of co-integration and long-run relationships among the variables. From the results, electricity supply and the NGP have a positive and significant relationship with real GDP. Consumer Price Index (CPI), a control variable, had a negative and significant relationship with real GDP in the short run. Also, the Error Correction Term is -0.2228. Consequently, the speed of adjustment for the model to return to long-run equilibrium after a shock is 22.28%. The study also found that the current two (2) percent annual growth rate of proven NG reserves is inadequate to cater for the rapid growth in the NG sector considering the positive effect of the NGP. The study, therefore, recommends that policymakers should continue to promote the NGP programme because it contributes positively to real GDP. In addition, consideration should be given to a diverse energy mix policy to reduce the over-reliance of electricity generation on NG-fired power plants, consequently, policymakers should promote policies that will lead to the diversification of the energy source for power generation.

A labor value model for exchange rates in a comprehensive economy

This paper establishes a labor value model of exchange rate. According to the labor theory of value, the exchange rate is decomposed into the exchange rate of “a country’s currency and a unit of product” to obtain the main body of the model, and the capital market adjustment items measured by official foreign exchange reserves are added to obtain the complete model. In the empirical part, the appropriate index was selected, and the cointegration relationship test was carried out, and the ARDL model was established according to the test. The regression coefficient R2 of the model is 98%, and the correlation coefficient between the fitted exchange rate value and the actual exchange rate value is 0.95.

Modeling the Farmer Exchange Rate in Indonesia Using the Vector Error Correction Model Method

The agricultural sector plays a crucial role in the Indonesian economy. However, the farm sector still has serious problems, including agricultural product prices, which often fall when the harvest supply is abundant. So often, the income obtained is not proportional to the price spent by farmers, which has an impact on decreasing the welfare of farmers. An indicator to observe changes in the interest of Indonesian farmers is the Farmer Exchange Rate Index (NTP). This study aims to form a model and project the welfare level of farmers in Indonesia, focusing on NTP indicators, which are caused by the influence of variables such as inflation, Gross Domestic Product (GDP), interest rates, and the rupiah exchange rate. The method used is the Vector Error Correction Model (VECM), used when there are indications that the research variables do not show stability at the initial level and there is a cointegration relationship. The results of this study show that in the long run, significant factors affecting NTP are inflation, interest rates, and the rupiah exchange rate. Meanwhile, in the short term, the variables that have an impact are GDP and the rupiah exchange rate. The resulting VECM model shows a MAPE error rate of 1.79%, indicating excellent performance, as the MAPE error rate is below 10%. The implication of this research is provides information related to NTP projection that can be used to formulate strategies to strengthen Indonesia's agricultural sector.

The impact of energy efficiency and renewable energy consumption on carbon emissions in G7 countries

ABSTRACT This study analyzes the impact of energy efficiency and renewable energy use on carbon emissions in G7 countries. The period examined covers the years 1971–2023. There are two important results. The first is the cointegration relationship between the variables. Accordingly, there is a long-run cointegration relationship between energy efficiency, renewable energy use and carbon emissions. The second is the direction and strength of this long-run relationship. When energy efficiency increases, carbon emissions decrease. This result is consistent with general expectations. On the other hand, there is a positive relationship between renewable energy consumption and carbon emissions. This result is not consistent with general expectations. The existence of a positive relationship between renewable energy consumption and carbon emissions is an issue that needs to be discussed.

How can there be an economic transition to a green ecosystem by adapting plastic-to-fuel technologies through renewable energy?

Plastics partially help define the way humanity lives today and plastic waste is an increasing phenomenon. It seems to be a serious concern that causes economic inefficiencies and environmental degradation that result in high CO2 emissions rates. To overcome this issue, regulations and policies on the production and behavior of plastics and energy should be reconsidered in the growing world economy. To this end, this study aims to foster economic pathways for decision-makers and answers the question of “How can there be an economic transition to a green eco-system by adapting plastic-to-fuel technologies through renewable energy?”. This strategy is introduced by an autoregressive distributed lag (ARDL) time-series methodology with the worldwide data from 1950 to 2022, considering energy production, economic growth, and CO2 emissions as independent variables; and plastic production as a dependent variable. The findings reveal the existence of a long-run cointegration relationship. An increase in renewable energy production (REP) and CO2 emissions causes a decrease in plastic production, while the opposite is valid for non-renewable energy production (NREP). The relationships between environmental degradation, energy production and economic growth are conducted through a global perspective. Therefore, this pioneering aspect of the article is expected to become a reference study for many studies in the future, in mitigating global warming by adapting plastic-to-fuel technologies. The paper shows how the production and use of renewable energy have critical importance in terms of the economic development levels of countries and therefore, can benefit countries (i.e., particularly developing and least developed countries) for environmentally friendly production and consumption patterns through a very important good: plastic.

An Empirical Study on Tourism and Economic Growth in Greece: An Autoregressive Distributed Lag Boundary Test Approach

The objective of this research is to analyze the impact of the tourism industry on the economic growth of Greece. The study employs empirical analysis and time series econometric techniques to evaluate the Tourism-Driven Growth hypothesis. Information spanning from 1995 to 2022 about the growth of tourism (TR), expenditure on tourism (TE), average expenditure on tourism per capita (PCTE), and economic growth (GDP) was utilized. Initially, the authors examined the interconnections among these variables using the Autoregressive Distributed Lag (ARDL) Bounds Test. After identifying a statistically significant cointegration relationship, the study proceeded to estimate the long-term and short-term coefficients associated with these variables. Based on the results, it appears that there is a long-term correlation between economic growth and tourism, indicating that international tourism can have a positive impact on economic expansion.

Asgari Ücret, Enflasyon ve Gelir Dağılımı İlişkisi: 40 Ülke Örneği

Today, it is generally accepted that there is a deterioration in income distribution. For this reason, many academic studies are carried out to better understand the factors affecting income distribution. On the other hand, an increase in inflation rates is observed as a result of the increased money supply, especially after the 2008 global economic crisis. This upward trend has brought the issue of inflation to the economic agenda of countries. However, there is no consensus on how the inflation environment affects income distribution. In the study, panel data analysis was conducted with data from 40 countries covering the period 2008-2020 in order to explain the relationship between inflation and income distribution. As a result of the cointegration test, no cointegration relationship was found between inflation and income distribution in the relevant period. However, countries' efforts to improve income distribution are also important. As an indicator of this effort, the countries were divided into two groups according to the increase rate of the minimum wage and the cointegration test was repeated. However, as a result of these tests, no cointegration relationship was found. The results obtained suggest that the relationship between inflation and income distribution has a structure that varies depending on other economic policies implemented in the country and the type of inflation, but the use of non-linear methods in subsequent studies may lead to different results.

Deep reinforcement learning applied to statistical arbitrage investment strategy on cryptomarket

In the complex and dynamic nature of financial markets where increasingly sophisticated investment strategies are required, deep reinforcement learning (DRL) has proven successful in generating real-time investment strategies, outperforming classical models. Alongside this, statistical arbitrage strategies exploit temporary market inefficiencies to generate returns. In this regard, a novel DRL-based arbitrage method has been developed.This paper presents a unified framework that combines classical statistical arbitrage theory with DRL framework to generate investment strategies. The framework addresses the challenges of identifying similar asset portfolios, extracting signals indicating temporary price deviations, and determining optimal trading rules given market conditions. The proposed methodology involves constructing arbitrage portfolios based on cointegration relationships, removing signals from price series and portfolios, and using a DRL agent to make optimal decisions within a fixed time horizon.The empirical analysis focuses on the cryptocurrency market, known for its volatility and risk. Results demonstrate that DRL agents can generate strategies with positive returns in out-of-sample periods 79.52% to 112.82% with no transaction cost, outperforming market benchmark Bitcoin 32.51% return, the best performing over the period Litecoin with 57.11% return and the worst performing Solana with a 35.70% loss. Moreover, these strategies effectively reduce risk, achieving higher risk-adjusted returns on individual assets. The strategies maintain positive returns when considering transaction costs, with the DRL agent outperforming the standard arbitrage strategy. The best-performing strategy is based on a Deep Q-Network (DQN) agent with a return of 18.39%, annualized volatility of 12.22%, and an annualized Sharpe ratio of 2.43. At the same time, Bitcoin holds an annualized volatility of 44.13% and a Sharpe ratio of 1.08.Their randomness and coherence are studied to verify the robustness of the agents’ decisions. The actions generated by the agents are not random but based on well-founded policies, which align with the obtained results.

Trade agreements and CO2 emissions in Asian countries: accounting for institutional heterogeneity.

This paper aims to measure the impact of environmental provisions in free-trade agreements on pollution levels in 40 Asian economies for the period 1990-2019. Following the failure of WTO negotiations, there has been a rapid proliferation of preferential trade agreements incorporating various types of environmental clauses. We exploit such changes to assess their influence on pollution emissions. We use a DOLS approach that considers the potential endogeneity of environmental clauses. Further, panel vector error correction models (VECM) are employed for examining the presence of a cointegration relationship among the variables studied. Overall, our findings indicate that these environmental clauses have heterogenous effects on CO2 emissions. We do not find significant effects of environmental clauses on pollution. However, this result is driven by environmental provisions that are not legally enforceable. We do find a positive effect of environmental clauses with a higher level of legalism on the environmental quality. These results show that the inclusion of environmental provisions in trade agreements is not sufficient by itself. Such provisions should incorporate a legally enforceable framework to effectively address environmental concerns. These findings have significant policy implications for Asian countries.

The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19

Can grand environmental goals still be achieved when the 1.5 °C climate target and Industry 4.0 encounter the COVID-19 pandemic? Exploring the impacts of artificial intelligence (AI) and crude oil shocks as critical drivers on clean energy stocks may provide fresh insights to tackle this question. This paper explores the asymmetric impacts of positive and negative changes in the AI index and different oil shocks on clean energy stock sub-sectors by adopting the non-linear autoregressive distributed lag (NARDL) model. Our investigation of a sample reveals a significant positive long-run relationship between the AI index and clean energy stocks. Additionally, the impacts of AI and oil shocks on clean energy stocks vary broadly across different sub-sectors. The COVID-19 pandemic has strengthened the cointegration relationship and reduced the long- or short-term asymmetry among the AI index and clean energy stocks. This study incorporates the AI index into the research framework of oil and clean energy, which has not been explored in previous research, and is of significant importance for clean energy investment and provides guidance for market participants.

The dynamic impact of oil shocks on the Saudi stock market: new evidence through dynamic simulated ARDL approach

PurposeThe paper investigates the dynamic relationship between oil prices, the USA dollar exchange rate and the Saudi stock market index.Design/methodology/approachThe authors perform a novel dynamic simulated the autoregressive distributed lag (ARDL) on weekly data from 2010 to 2021.FindingsThe authors' work reveals three main results: First, a cointegration relationship exists between oil prices and the Saudi stock market index. Second, the Saudi stock market is strongly affected by fluctuations in oil prices in both the short and long run. Third, the exchange rate of the USA dollar has a slight influence on the movements of the Saudi stock market. The simulations show that the Saudi stock market index has a long-run upward trend after an oil price shock, while the dollar index rises moderately after a similar shock. Moreover, the first months of the COVID-19 pandemic coincided with a significant decline in the Saudi stock market index, particularly the substantial drop in oil prices.Practical implicationsThese findings encourage domestic and foreign investors to benefit from an upward trend in oil prices, especially after the opening of the Saudi market to foreign investment. On the other hand, it raises questions about the Saudi economy's dependence on oil as the sole vehicle for output growth. It highlights the urgent need for diversification and productivity growth in the non-oil sector and other renewable natural resources to increase Saudi competitiveness.Originality/valueThe novelty of the research lies in the following. First, the authors apply one of the latest developments in time-series modeling techniques. This dynamic ARDL simulation model provides a worthwhile alternative way to explore dynamic correlations in the short and long run and assess the choc effects. Secondly, the study would enable us to track the impact of the COVID-19 health crisis on the Saudi stock market.

Johansen’s Cointegration Analysis of Some Factors of Economic Growth and Exports of Products from the Republic of Azerbaijan to Ukraine

In the present paper, a Johansen’s cointegration analysis is carried out considering the volume of exports from Azerbaijan to Ukraine, GDP per capita of Ukraine, the openness of the economy of Ukraine and the economically active population of Azerbaijan for the period 1996-2022, also a comparative analysis of the above indicators is carried out, the characteristics of joint short-term and long-term movements are determined. In the course of research the author used the methodology of modified gravitational modeling, the econometric methodology of time series analysis, including tests for checking stationarity, the extended Dickey-Fuller test, the Granger test for the detection of causality, determining the cointegration dependence using the Johansen’s test, a Vector Error Correction Model (VECM) has been built. First, a basic modified model of gravity has been built, and the statistical adequacy of this model has been checked. A comparative analysis of two regression models was carried out after the inclusion of the trend component in one of them. The dynamic structure of regression residuals was studied and the test for heteroscedasticity and autocorrelation was carried out. It is shown that the most suitable specification for cointegration is the quadratic trend in the initial levels with a linear trend in cointegration relationships, which has led to the emergence of two cointegration vectors. As a result of the completed analysis, two cointegration relationships are obtained. The results of the impulse response functions, decomposition of dispersions and the VECM model in the form of combinations of two cointegrating vectors with the expected signs of the adjustment integers showed that the economic indicators used in the analysis for the specified period maintain cointegration in the long term with the not stable equilibrium joint movements of the factors under study.

Study on the Influencing Factors of the Competitive Network Pattern of Cobalt Industry Chain Trade in the Context of Big Data Analysis

Abstract The article proposes a time series model to explore the influencing factors of the cobalt industry chain trade competition network pattern. By analyzing the current situation of the evolution of the cobalt industry chain trade network, the relevant variables are selected. The data are described based on the overview of the relevant algorithmic research of the time series model. Finally, the model based on the time series data is empirically tested. The unit root is verified to be in an unsteady state by first-order differencing, and the p-values all have a probability of accepting the original hypothesis greater than 0. After the second-order differencing ADF test, all the series are smooth and all of them are second-order monotonic. After the cointegration test, it was found that the residual series is smooth at the 5% critical level, and there is a cointegration relationship.

The Impact of Artificial Intelligence Applications on Digital Banking in Turkish Banking Industry

This paper presents the effects of chat box (C.B.) and video call (V.C.) artificial intelligence applications, which are becoming increasingly common in the banking system in parallel with the development of information systems on the number of digital banking users, and the relationship between the variables is investigated. The data set of the variables consists of a total of 44 observation points between the 1st quarter of 2012 and the 4th quarter of 2022. The cointegration relationship between the variables is examined using the ARDL cointegration test. According to the F statistic results for cointegration hypothesis testing, there is a statistically significant relationship between the variables at a 1% significance level. In the long run, a 1% increase in Chat Box users causes an increase of approximately 0.2% in the number of mobile banking users. A 1% increase in Chat Box users in the short term increases the number of mobile banking users in the current period by approximately 0.03%. A 1% increase in video call users in the long term causes an increase of approximately 0.04% in the number of mobile banking users. A 1% increase in the number of video calls in the current period increases current period mobile banking applications by approximately 0.005%. It has been determined that the effect of chat box applications on mobile banking is higher than that of video call applications. In addition, there is a close correlation between mobile banking and the independent variable chat box, no correlation between mobile banking and video calling, and a moderate correlation between chat box and video calling. All findings mean that artificial intelligence applications positively contribute to the expansion of mobile banking.