Long-term Cointegration Research Articles

Are natural resource volatility curses or blessings for economic performance? Stories of resource-rich regions

This paper analyses the time-varying and non-linear effects of natural resource volatility on economic growth in 10 countries using the smooth transition regression (STR) model and further examines the long-run and short-run relationships using the autoregressive distributed lag (ARDL) cointegration approach. The conclusions exhibit that natural resource rents are linearly and non-linearly associated with economic growth in countries with different levels of economic development, and display long-term cointegration and short-term equilibrium state. This study provides guides for policymakers in considering differences in national development to implement management strategies that combine the long and short term.

The Dynamics of Tourism, Economic Growth, and CO2 Emissions in Uzbekistan: An ARDL Approach

In this study, we examined the impact of key macroeconomic variables on the sustainability of tourism in Uzbekistan using open data from the World Bank from 2000 to 2023. The selected variables for the study include the number of international tourists, GDP, CO2 emissions, and exchange rates. Considering the importance of climate issues in ensuring sustainable tourism, the study combines these factors using the autoregressive distributed lag (ARDL) model to assess their long-term relationships. We chose this model due to its flexibility in dealing with variables of different integration orders. The results of the bounds testing procedure confirm the existence of a long-term cointegration relationship between the dependent variable (international tourist arrivals) and the selected macroeconomic indicators. Specifically, the study results indicate that GDP growth, exchange rate fluctuations, and CO2 emissions positively affect tourist arrivals to Uzbekistan. The findings of this study underscore the importance of infrastructure development and macroeconomic stability in attracting international tourists while emphasizing the need for sustainable international practices to mitigate environmental impacts. This study contributes to understanding the complex interrelationship between tourism development and macroeconomic variables in the context of developing countries and aids decision-making in this area.

Interconnected Stock Markets: Analysing Cointegration and Correlation among Global Stock Indices

This study examines the long-term and short-term relationships among five major global stock indices—NSE NIFTY (India), S&P 500 (USA), FTSE 100 (UK), Hang Seng (Hong Kong), and Nikkei 225 (Japan)—over the period from 2008 to 2023. Using advanced econometric techniques such as Johansen cointegration tests, Engle-Granger residual cointegration test and the Vector Error Correction Model (VECM), the research explores the degree of interconnectedness and cointegration among these indices. The results indicate strong positive correlations between NSE NIFTY, S&P 500, and Nikkei 225, suggesting a growing synchronization of these markets due to global economic integration. Johansen's test reveals the presence of multiple long-term equilibrium relationships, affirming that despite short-term deviations, these markets tend to move together in response to global macroeconomic factors. The analysis further highlights that the S&P 500 and FTSE 100 indices exhibit significant error correction mechanisms, underscoring their leadership role in global market dynamics. In contrast, NSE NIFTY shows a weaker adjustment toward long-term equilibrium, indicating its reactive nature to changes in other global indices, particularly the S&P 500. The study finds that while Hang Seng and Nikkei 225 are connected globally, their short-term linkages appear weaker, likely influenced by regional factors. Overall, the results emphasize the critical influence of the U.S. and Japanese markets on NSE NIFTY, with both long-term cointegration and short-term market dynamics shaping the Indian stock market’s movements. This research offers key insights for investors seeking to understand global stock market interdependencies and manage portfolio risk.

Evaluating the Effectiveness of Macroeconomic Determinants on the Performance of the Dhaka Stock Exchange: A Time Series Approach

The present study examines the causal relationships between selected macroeconomic factors and the Dhaka Stock Exchange Broad Index (DSEX) performance from January 2014 to December 2023. Time series econometric techniques include Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to verify stationarity, Granger causality to investigate short-term dynamics, and Johansen's co-integration test to evaluate long-term relationships. The results show that while there is no long-term cointegration, there are significant short-term relationships between macroeconomic variables and the DSEX broad index. GDP growth and unemployment significantly impact stock performance, with two-way causality. While interest rates and the money supply have little impact on the index, inflation forecasts trends in inflation even if it doesn't directly influence it. Overall, the DSEX broad index shows how important it is to Bangladesh's macroeconomic dynamics by accurately predicting changes in GDP growth, unemployment, and inflation. The researcher provides unique insights into the mechanics of the Bangladeshi stock market, filling a vacuum in the literature on emerging countries. It analyzes how macroeconomic conditions affect the DSEX index over time, including the COVID-19 pandemic. The study investigates the influence of several macroeconomic variables on DSEX Index performance, providing significant information for Bangladeshi investors and policymakers looking to increase profits, inform policy choices, and promote economic stability. To have a better knowledge of stock market dynamics, future studies should include other macroeconomic aspects and regional markets.

Short-term and long-term cointegration analysis of foreign direct investment and value added generation in the manufacturing industry

The contraction of manufacturing economic activity in Latin American countries has been affected by the health crisis in the last few years. This phenomenon has negatively impacted the Latin American countries’ economies. In order to evaluate the impact of the manufacturing economy, this research integrates the impact of Foreign Direct Investment (FDI) on the growth of the Ecuadorian manufacturing sector, from 1981 to 2019, considering the role of the state through public spending using cointegration. The results are not consistent considering the empirical framework used; thus, FDI has a negative and significant influence on the manufacturing sector. Also, the manufacturing sector has a strong relationship with FDI in the short run and a less significant one in the long run. The results presented in this research suggest promoting domestic and FDI in the manufacturing sector, not only towards overexploited and monopolized sectors such as mining and telecommunications.

The Relationship between Agricultural Production, Agricultural Exports and Economic Growth in Türkiye: Cointegration and Causality Analysis

This study aimed to assess the influence of agricultural production and agricultural product exports on economic growth within the Turkish economy. To achieve this, annual data spanning from 1980 to 2022 were utilized to construct a model, employing the ARDL Bounds Test to discern the long-term cointegration relationship among variables and the Fourier Toda-Yamamoto Causality Test to determine causality relationships. The empirical findings from the ARDL Bounds Test indicate that increases in both agricultural production and agricultural product exports positively impact economic growth. Moreover, results from the Fourier Toda-Yamamoto Causality Test unveil a two-way causal relationship between agricultural production and GDP, while a one-way causality relationship is observed between agricultural product exports and GDP. Despite the declining share of the agricultural sector in GDP over the years in Turkey, these findings underscore the enduring importance of agriculture in driving economic growth and creating value. As potential solutions to Turkey's balance of payments challenges, strategies such as boosting domestic demand, expanding the domestic market, and augmenting agricultural sector contributions to foreign exchange earnings through agricultural export subsidies are proposed. Furthermore, efforts to mitigate income disparities between the agricultural sector and other sectors could foster socio-economic development in rural areas, thus promoting a more equitable distribution of national income across society.

Powering Growth: The Dynamic Impact of Renewable Energy on GDP in ASEAN-5

This study investigates the dynamic relationships between renewable energy consumption, labor force, gross fixed capital (GFC), and GDP across ASEAN-5 nations from 1984 to 2020. Utilizing data from the World Development Indicators (WDI), the Energy Information Agency (EIA), and national labor statistics, we employ unit root tests, ARDL bound testing for cointegration, and Toda-Yamamoto causality procedures. Our findings indicate significant long-term cointegration between the studied variables in Indonesia and the Philippines, suggesting persistent economic relationships, while results for Malaysia show no cointegration and remain inconclusive for Thailand. The economic analysis reveals that GFC robustly drives GDP growth across these countries, whereas the impacts from labor force and renewable energy consumption are more variable. Causality tests further demonstrate that renewable energy consumption significantly fosters GDP growth in Indonesia, Malaysia, and Singapore, aligning with the growth hypothesis. Conversely, findings for the Philippines and Thailand support the neutrality hypothesis, indicating no direct causal impacts. These insights underline the crucial role of tailored renewable energy strategies to enhance economic growth and sustainability in ASEAN-5, providing valuable policy implications for regional energy governance.

Structural Effects of Economic Shocks on the Macroeconomic Economy–Electricity–Emissions Nexus in India via Long-Term Cointegration Approach

For developing nations to achieve net-zero targets, macroeconomic linkages impacting the decoupling of emissions from economic growth must account for non-linear business cycles and economic shocks. This study aims to delineate decarbonization policy pathways for the Indian electricity sector in the aftermath of COVID-19 by analysing the long-term evolution of the economy–electricity–emissions (3E) nexus during the 2008 financial crisis and during COVID-19, covering the period of 1996Q2 to 2020Q3. Upon testing multiple theoretical 3E systems, it was found that a model internalizing trade, inflation, and stochasticity was able to minimize the reproduction errors from growth to recession phases, as well as predict the rebound effect from an economic crisis. This was revealed to be due to more information within the coefficients in a trade stochastic model. Our results confirm the existence of electricity-associated emission decoupling with capital formation in the long-run, post-crisis, while economic growth and inflation increase CO2 emissions. The main finding highlights the negative feedback loop of inflation->trade->emissions, which shows that GDP and emissions are not directly causal. This long-run macroeconomic dynamic death spiral causes decoupling to be inhibited, where fossil fuel imports should not be subsidized for economic shock rebound, and the risk hedging of energy transition investments should occur in the post-COVID-19 era.

Econometric Analysis of South Africa’s Fiscal and Monetary Policy Effects on Economic Growth from 1980 to 2022

This study examined South Africa’s economic growth rate from 1980 to 2022 through an econometric analysis of fiscal and monetary policies. The study sought to investigate the relationships between the economy’s growth rate and various fiscal and monetary policy variables, taking into account different economic approaches such as Keynesian, monetarist, and Wagner’s perspectives. The methodology used consisted of data preparation, multiple unit root tests, Autoregressive Distributed Lag (ARDL) cointegration analysis, diagnostic tests, and pairwise Granger causality analysis. The empirical analysis found a long-term cointegration among the economic growth rate, government debt, expenditure, and revenue in fiscal policy, though government debt and expenditure were not statistically significant. Contrary to economic theory, increased government revenue had a negative correlation with economic growth. There was no long-term relationship found between the economic growth rate and monetary policy variables such as the official exchange rate, inflation rate, real interest rates, and M3 money supply. Pairwise Granger causality tests revealed a one-way relationship between government spending and economic growth, providing support to the Keynesian approach to fiscal policy. This study also discovered evidence that economic growth Granger-causes inflation, implying that economic growth may have predictive power for inflation, consistent with the demand-pull inflation hypothesis. However, no direct predictive relationships were found between the selected monetary policy variables and economic growth, supporting the long-run theory of monetary neutrality. This study suggests evaluating spending, managing inflation, implementing reforms, closing infrastructure gaps, encouraging investment, and ensuring fiscal sustainability.

Impacts of economic development, globalization, and gross capital formation on natural resources rents: Evidence from India

Natural resources are a country's wealth, and resources economics and policy literature has extensively researched how to utilize this richness as a determinant of the country's growth. However, the primary challenges reside in establishing economic development as a driver for natural resource rents. Given this context, this study explores the association between economic development, globalization, gross capital formation, and natural resources rents in India. For the empirical research, the study employed Dynamic Auto-Regressive Distributive Lag (DARDL) simulation and Kernel-based Regularized Least Squares (KRLS) approaches on yearly data from 1990 to 2020. The present research used a unit root test to ascertain the stationary state across all variables, establishing their long-term cointegration connection. Results suggest that capital formation and economic development positively impact rents derived from natural resources in India, while globalization negatively affects natural resources rents. Unidirectional causation of economic development, globalization, and gross capital creation to natural resources rents has been established in this study using the Toda-Yamamoto causality model. Our findings have important policy implications and support the idea that sustainable economic growth models inherently rely on factors like the efficient utilization of natural resources and globalization.

Road map from dirty growth to sustainable green growth in Turkiye: Management of trade and financial processes?

This study examines how business, financial, monetary, and trade freedom influence Turkiye's green growth from 1995 to 2022, utilizing the ARDL approach to cointegration. Our results confirm the long-term cointegration among the variables. Robustness tests, such as Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), consistently show that business and trade freedom hinder Turkiye's green growth. Financial freedom and monetary freedom consistently foster it. Business freedom and heightened trade freedom lead to increased fossil fuel consumption, whereas monetary freedom stabilizes Turkiye's currency, and financial freedom promotes entrepreneurship. Enhancing eco-friendly energy sources and investing in green technologies are crucial for promoting sustainable growth, reducing production costs, fostering entrepreneurship, and encouraging competition. The fact that the impact of these variables on green growth in Turkiye has not been studied before makes the study's findings novel.

Exploring the Linkage Effects between Coking Coal Futures and Carbon Emission Rights Prices under the Dual-Carbon Framework

Coking coal is a vital energy resource for economic development, with fluctuations in coking coal futures prices significantly guiding spot prices. Additionally, variations in carbon emission rights prices directly affect China's energy conservation and emission reduction efforts. This paper examines the daily trading prices of carbon emission rights and coking coal futures. It utilizes various tests, including the ADF test and cointegration test, and constructs a two-dimensional vector autoregression (VAR) model with a two-period lag. The paper also performs an impulse response function analysis and variance decomposition. Empirical results reveal: First, the daily trading prices of coking coal futures have exhibited a fluctuating upward trend over the past six years; Second, there is a long-term cointegration relationship between coking coal futures prices and carbon emission rights prices; Third, there is no Granger causality between the two; Fourth, fluctuations in carbon emission rights prices have a stronger guiding effect on coking coal futures prices than vice versa. Recommendations include: (1) Strengthening oversight of the coking coal futures market and leveraging policy guidance to prevent extreme prices; (2) Systematically including more participants in the carbon market, optimizing the trading structure, and promoting healthy market development; (3) When developing policies for carbon emission rights, the government should mitigate the impact of price fluctuations on other sectors.

Macroeconomic econometric analysis of government policy

This paper first proposes three indicators, gross domestic product (GDP), consumer price index (CPI) and producer price index (PPI), to quantitatively describe the macroeconomic situation based on the theory of macroeconomic indicators. Next, in view of the fiscal and monetary policies promulgated by the Chinese government, two indicators, namely government budget expenditure, and new credit, are selected to quantitatively describe the government's economic policies. Then the Granger causality test and Johansen cointegration relationship analysis are conducted between these indicators. The study found that there is no significant causal relationship between China's government budget expenditure, new loans and GDP, but the long-term cointegration relationship is obvious, and this relationship is positive. And found that there is not only a significant bi-causal relationship between the price indexes (CPI, PPI), but also a long-term positive cointegration relationship. On this basis, a VEC model between government budget expenditure and GDP, CPI and PPI was established based on the integrity of the indicators, and a VAR model was stablished between the amount of credit and GDP, CPI and PPI.

The Urban-rural Income Gap, Urbanization, the Upgrading of Industrial Structure, and Agricultural Labor Productivity

Agricultural labor productivity reflects the relationship between labor factor input and economy in the agricultural industry, and improving agricultural labor productivity is an important way to achieve agricultural modernization and high-quality agricultural development. Based on the VAR model and Granger causality test, this paper conducts an empirical analysis of the impact of urban-rural income gap, urbanization, industrial structure upgrading, and agricultural labor productivity in China from 1981 to 2020, and concludes that there is a long-term cointegration relationship between urban and rural income gap, urbanization and industrial structure upgrading and agricultural labor productivity. In the long run, the urban-rural income gap and industrial structure upgrading have a positive impact on agricultural labor productivity. Urbanization has a negative impact on the improvement of agricultural labor productivity, so relevant suggestions are given.

An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading.

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period.

The Impact of Financial Development on Bank Investment in Iraq

Theoretically, the literature indicates that financial development has a positive effect on investment. therefore this study aim to determine the impact of the financial development on investment banking in iraq, during the semi-annual period (2005-2021) using autoregressive distributed lag (ardl), and using the toda yamamoto causal test, the result of the bound test (ardl) showed a long-term cointegration relationship between variables , the model showed that the financial depth positively affect the investment banks against the negative effect of the long-term financial stability index, while toda yamamoto causal test showed that there is no causal relationship between financial development and investment banking.

The Impact of China’s Capital Account Liberalization on the Direction of Cross-border Capital Flows

Objective: This article aims to investigate the impact of China’s capital account openness (OPEN) on cross-border capital flows (CCF). Methods: The research utilizes the ARDL-ECM model for empirical analysis. It begins by summarizing existing literature on the topic and then proceeds to analyze the data spanning from 1998 to 2023. The study explores the long-term cointegration relationship between OPEN and CCF. Additionally, it investigates the correlation between the direction of openness and its effect on CCF. Notably, the research introduces the concept of the volatility of OPEN as an explanatory variable to understand its impact on various types of capital flows. Results: The findings reveal a significant long-term cointegration relationship between OPEN and CCF. This relationship is significantly influenced by the direction of openness. Moreover, the study identifies that the volatility of OPEN notably affects different categories of capital flows. Furthermore, it observes that structural changes in net foreign securities investment are comparatively higher than those in net foreign direct investment. Conclusion: This study underscores several recommendations for the ongoing process of capital account liberalization in China. Firstly, adjust OPEN flexibly to promote or restrict specific types of capital flows. Adopt elastic capital control measures for different types of capital flows to balance the stability of domestic capital markets and the activity of international capital flows. Secondly, careful adjust capital outflow openness to maintain economic environmental stability and prevent capital flight. Lastly, encourage long-term investment, particularly in the real economy and infrastructure sectors, and provide incentives for investors committed to long-term investment.

Impact of Money Supply and Inflation on Economic Growth

Understanding the impact of money supply and inflation on Nepal's economic development is the main objective of the current study. This work aims to give empirical backing to the current debate over money supply, inflation, and the relationship between these factors and economic growth. More research has been done recently on the relationship between money supply and economic growth than on any other topic in the field of monetary economics. A price increase that, over time, results in a decline in buying power is known as inflation. This study looks at how inflation and the money supply affected economic development between 1974–1975 and 2022–2023. When establishing if there is long-term co-integration among variables when the bound test has been applied, the ARDL co-integration approach is employed in the study to ascertain the link between the variables. GDP is the dependent variable, and the independent variables are broad money (M2) and consumer price inflation (CPI), respectively. The notion that inflation is always a monetary event needs to be revisited and reevaluated, particularly in light of the distinct dynamics of inflation in developing economies. The research indicates a positive correlation between CPI and economic growth. The data indicates that CPI has a significant influence on economic growth in emerging nations like Nepal.

International Portfolio Diversification Benefits: An Empirical Investigation of the 28 European Stock Markets During the Period 2014–2024

Abstract This study investigates the benefits of international diversification in the stock markets of the 28 European countries (the EU and the UK) over two five-year periods: a stable period from 2014 to 2019 and a turbulent period from 2019 to 2024. The analysis draws on the Markowitz mean-variance, Sharpe reward-to-variability, and naive diversification models, based on which different investment strategies were developed and implemented. We find that actively managed portfolios perform significantly better than naively diversified portfolios. The analyzed markets exhibit positive short-term associations, with an average correlation coefficient of 0.29 in the first period and 0.46 in the second period. However, these markets do not show long-term cointegration. Recent crises have reduced diversification benefits, yet significant opportunities for diversification remain. Diversification benefits are almost halved in the second period: average single-market standard deviation can be reduced by 60.5% with investments in 20-indices portfolios in the stable period, and only by 33.7% with the same portfolio size in the turbulent period.

Effectiveness of hierarchical medical system and economic growth: based on China's urban vs. rural health perspectives.

The hierarchical medical system is an important measure to promote equitable healthcare and sustain economic development. As the population's consumption level rises, the demand for healthcare services also increases. Based on urban and rural perspectives in China, this study aims to investigate the effectiveness of the hierarchical medical system and its relationship with economic development in China. The study analyses panel data collected from Chinese government authorities, covering the period from 2009 to 2022. According to China's regional development policy, China is divided into the following regions: Eastern, Middle, Western, and Northeastern. Urban and rural component factors were downscaled using principal component analysis (PCA). The factor score formula combined with Urban-rural disparity rate (ΔD) were utilized to construct models for evaluating the effectiveness of the hierarchical medical system from an urban-rural perspective. A Vector Autoregression model is then constructed to analyze the dynamic relationship between the effects of the hierarchical medical system and economic growth, and to predict potential future changes. Three principal factors were extracted. The contributions of the three principal factors were 38.132, 27.662, and 23.028%. In 2021, the hierarchical medical systems worked well in Henan (F = 47245.887), Shandong (F = 45999.640), and Guangdong (F = 42856.163). The Northeast (ΔDmax = 18.77%) and Eastern region (ΔDmax = 26.04%) had smaller disparities than the Middle (ΔDmax = 49.25%) and Western region (ΔDmax = 56.70%). Vector autoregression model reveals a long-term cointegration relationship between economic development and the healthcare burden for both urban and rural residents (βurban = 3.09, βrural = 3.66), as well as the number of individuals receiving health education (β = -0.3492). Both the Granger causality test and impulse response analysis validate the existence of a substantial time lag between the impact of the hierarchical medical system and economic growth. Residents in urban areas are more affected by economic factors, while those in rural areas are more influenced by time considerations. The urban rural disparity in the hierarchical medical system is associated with the level of economic development of the region. When formulating policies for economically relevant hierarchical medical systems, it is important to consider the impact of longer lags.