Unit Root Problem Research Articles

Measuring How AI Innovations and Financial Accessibility Influence Environmental Sustainability in the G-7: The Role of Globalization with Panel ARDL and Quantile Regression Analysis

This study investigates the impact of AI innovation on environmental sustainability in the G-7 region from 2010 to 2022. Additionally, it tests the Load Capacity Curve (LCC) hypothesis in relation to financial accessibility, globalization, and urbanization. Cross-sectional dependence and slope homogeneity tests reveal the presence of cross-sectional dependence and heterogeneity issues. Panel unit root and panel cointegration tests confirm that the variables are free from unit root problems and are cointegrated in the long run. To identify significant factors influencing environmental sustainability, this study employs Panel ARDL and Quantile Regression methods. Both methods confirm the LCC hypothesis in the G-7 region, demonstrating a U-shaped relationship between income and the load capacity factor. The results indicate that AI innovation and financial accessibility are significantly positively correlated with the load capacity factor, while globalization and urbanization are negatively correlated, leading to lower environmental sustainability. To validate the robustness of the Panel ARDL and Quantile Regression results, Driscoll-Kraay standard errors, Augmented Mean Group, and Common Correlated Effects Mean Group estimation approaches are applied, all of which support the initial findings. Furthermore, the D-H causality test reveals unidirectional causality from economic growth, financial accessibility, globalization, and urbanization to the load capacity factor, and bidirectional causality between AI innovation and the load capacity factor.

Determinants of environmental sustainability in the United States: analyzing the role of financial development and stock market capitalization using LCC framework

This research investigates how the USA's load capacity factor (LCF) has been impacted by trade openness, financial development, stock market capitalization, and industrialization over the period 1990–2022. This study also tests the “Load Capacity Curve (LCC)” hypothesis. Various unit root tests were conducted to determine the stationarity of the dataset, revealing that the variables are free from unit root problems and exhibit mixed orders of integration. The “Autoregressive Distributed Lag (ARDL)” bounds test confirmed co-integration among the variables. The results from the ARDL model validated the existence of the LCC hypothesis in the USA. The findings also demonstrated that industrialization is positively correlated with LCF, whereas financial development, stock market capitalization, and trade openness are negatively correlated with LCF. To ensure the robustness of the ARDL estimations, the study also employed several regressions, all of which confirmed the validity of the ARDL results. Additionally, pairwise Granger causality tests revealed unidirectional causal relationships between GDP and LCF, stock market capitalization and LCF, and industrialization and LCF, while no causal relationships were found between financial development and LCF and trade openness and LCF. The findings advocate that the USA should advance eco-friendly industrial practices to ensure environmental sustainability, alongside regulated financial markets and institutions that mandate the utilization of green investments.

Exploring the LCC Hypothesis in the Nordic Region: The Role of AI Innovation, Environmental Taxes, and Financial Accessibility via Panel ARDL

This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.

Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach

This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also indicate that AI innovation and financial development have a significant positive correlation with the load capacity factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors.

Analyzing the Nexus between AI Innovation and Ecological Footprint in Nordic Region: Impact of Banking Development and Stock Market Capitalization using Panel ARDL method

This study investigates the impact of Artificial Intelligence (AI) innovation on the ecological footprint in the Nordic region from 1990 to 2020, alongside the effects of banking development, stock market capitalization, economic growth, and urbanization. Utilizing the STIRPAT model, the study incorporates cross-sectional dependence and slope homogeneity tests, revealing issues of heterogeneity and cross-sectional dependence. The analysis employs both first and second-generation panel unit root tests, confirming that the variables are free from unit root problems. Panel cointegration tests demonstrate that the variables are cointegrated in the long run. To explore the short- and long-term relationships, the study utilizes the Panel Autoregressive Distributed Lag (ARDL) model. The Panel ARDL results indicate that economic growth, stock market capitalization, and urbanization positively correlate with the ecological footprint in both the short and long run. Conversely, AI innovation and banking development negatively correlate with the ecological footprint. To validate the Panel ARDL estimations, robustness checks are performed using Fully Modified OLS, Dynamic OLS, and Fixed Effects with OLS, all of which support the initial findings. Furthermore, the study employs the D-H causality test to identify causal relationships. The results show a unidirectional causal relationship between AI innovation, stock market capitalization, urbanization, and the ecological footprint. In contrast, a bidirectional causal relationship exists between economic growth and the ecological footprint, as well as between banking development and the ecological footprint.

Advancing environmental sustainability in the G-7: The impact of the digital economy, technological innovation, and financial accessibility using panel ARDL approach

This study examines the impact of the digital economy, technological innovation, financial accessibility, and urbanization on CO2 emissions in the G-7 region from 1990 to 2019. The analysis employed Cross-Sectional Dependence (CSD) and Slope Homogeneity tests, revealing the presence of CSD issues and heterogeneous slope coefficients. First- and second-generation panel unit root tests indicated no unit root problem within the dataset, with variables showing mixed integration orders. Panel cointegration tests confirmed that the variables are cointegrated over the long run. To assess the short-run and long-run impacts of the explanatory variables on CO2 emissions, the study utilized the Panel Autoregressive Distributed Lag (ARDL) model. The findings indicate that the digital economy significantly reduces CO2 emissions, while economic growth, technological innovation, financial accessibility, and urbanization increase CO2 emissions in the G-7 region. The robustness of the Panel ARDL results was validated using Driscoll-Kraay standard errors, Augmented Mean Group (AMG), and Common Correlated Effects Mean Group (CCEMG) estimations. Additionally, the Dumitrescu-Hurlin causality test revealed a unidirectional causal relationship between the digital economy and CO2 emissions, GDP and CO2 emissions, and CO2 emissions and technological innovation. Furthermore, bidirectional causality was found between financial accessibility and CO2 emissions, as well as between urbanization and CO2 emissions. These findings provide comprehensive insights into the dynamic interactions between economic, technological, and environmental variables in the G-7 region, highlighting the complexity of achieving sustainable development.

Impact of Crop Production on Economic Growth in Nigeria, 1981 – 2018

This study examined the impact of crop production on economic growth in Nigeria utilizing annual time series data from 1981 to 2018. The study used Johansen cointegration test, Vector Error Correction test, the impulse response functions and variance decomposition for data analysis after the data series were checked against unit root problem using the Augmented Dickey Fuller unit root test. The study tested and found that increase in the production of cassava, cocoyam, groundnut, guinea corn, maize, millet, rice and yam spurs economic growth in Nigeria in the long-run except for cotton production and with only groundnut production contributing significantly to the output of the Nigerian economy in the short- run. The study, therefore recommends that the Nigerian government should encourage farmers in the production of crops through supply of fertilizer to increase their productivity and food output while improving their use of modern seedlings and technology in the production that may in turn increase the crop production while farmers should be encouraged to adapt best practices in farming to maximize crop yield.

Does Tourism Development Influence the Economic Growth in Pakistan? Evidence from ARDL and Causality Approach

This study analyzes the influence of tourism development on economic growth (GDP) in the case of Pakistan's economy using the time series data covering the spanning from 1995-2022. In order to unveil reliable and robust results, a more appropriate time-series approach was applied for the current article. Time series unit root tests infer that all the variables have unit root problems at levels I(0), however, after taking the first difference I(1), all series turn into following the stationary process. Furthermore, (ARDL) bound test reveals that a long-term cointegration prevails among all analyzed variables. The Granger causality test is also utilized to check the causal relationship among the series. The long-run elasticity estimates examine a statistically significant and positive link between the sect tourism sector GDP growth in the case of Pakistan. Furthermore, political instability and inflation rate have an inverse influence on GDP growth, whereas, capital investment has a positively significant influence on GDP growth. In addition, it is concluded that tourism sector development helps to move upward shift in the economy of Pakistan by generating more profits for the country. Finally, it is suggested that goverment should have to focus on investing in the tourism sector and provide better facilities for tourists.

Assessing Country Risk in the Stock Market and Economic Growth Nexus: Fresh Insights from Bootstrap Panel Causality

Investigating the nexus between the stock market and the real economy is vital, as financial markets can play a critical role in economic growth. However, the country's risk factors (political, economic and financial) influence economic growth and financial markets’ nexus. This study examines the role of stock market development and country risk (political, economic and financial) on economic growth, and the moderating influence of country risk in the stock market and economic growth nexus in BRICS economies (except Russia) over the period of 2000 to 2020. The study employed various latest econometrics techniques, including Covariate Augmented Dickey-Fuller (CADF) for identifying unit root problem, Westerlund (2007) test for examining long-run relationship, Cross-sectional auto-distributive lag (CS-ARDL) for estimating long-run relationship, and finally, Konya (2006) for identifying the causal relationship among the variables. The study explored that financial risk, economic risk and political risk is negatively associated with economic growth. However, stock market does not play a significant role in economic growth in this case. The study highlighted a bi-directional causality between economic growth and stock market development. The study also suggests a unidirectional causality from political, economic and financial risk towards economic growth. Finally, the study suggests some policy recommendations based on the empirical results.

Investigating the Symmetric Effects of Working Capital on Profitability in Turkish Banking: An ARDL Empirical Analysis

Abstract This paper carried out an empirical study on Return on Assets (ROA), total assets turnover, financial leverage, working capital, and debt-to-equity ratio. This study investigated commercial banks operating in Turkey. A secondary data collection method is used for the data collected from fifteen commercial banks. These banks' financial reports were examined from 2011 to 2022. A line graph is presented for each balance that tells us the volatility ratio in the variables, the unit root test applied to resolve the unit root problem, and the cointegration test used for long-term relationships among variables. The Autoregressive Distributed Lag (ARDL) bound test was performed to measure the short-run and long-run effects. Financial leverage, debt-to-equity ratio, and working capital have short- and long-run effects on ROA. Total asset turnover has only long-run effects on ROA in the Turkish banking industry. This study will be beneficial for investment managers, financial specialists, treasurers, and controllers of the banking industry.

Globalization and Economic Growth in Nepal A VECM Approach

This study explores globalization's political, social, and economic aspects and its impact on Nepal's economic growth between 1990 and 2020. The study employed sophisticated econometric techniques, first utilizing the Johansen Cointegration and VECM to assess causality among the variables under investigation and the Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests to confirm the presence of unit root problems. The ADF and PP test findings showed that every variable is integrated at first difference. One cointegrating vector was found by the Johansen Cointegration test, indicating a significant long run association among the variables. The study discovered a one-way causality between economic growth and political and economic globalization in the short-run analysis. On the other hand, over time, there is a two-way causality between Nepal's economic growth and political and economic globalization while a one-way causal relationship exists between social globalization and economic growth. These results recommended that Nepal's economic growth immediately impacts how it integrates into the global economy in several sectors. Economic and political globalization influences and is influenced by Nepal's economic growth in the short term, indicating a reciprocal link. The study found that there is need for a balanced approach to globalization that fosters economic growth while reducing its negative repercussions. It also stresses how crucial it is for Nepal to actively participate in the political and economic spheres of the world.

Analysing the impact of FDI and Institutional Quality on Economic growth in South Asian: Panel ARDL APPROACH

This study investigates the impact of institutional quality and foreign direct investment (FDI) on the economic growth of South Asia from 1996 to 2021. The analysis employs various statistical techniques, including panel unit root testing, panel cointegration testing, and Panel Autoregressive Distributive Lag (PARDL) models, to gain insights into the region's economic development. For finding the association between institutional quality, FDI, domestic investment and economic growth in South Asia, first tried to check the unit root problem in the selected series by employing the panel unit testing and results of these tests conclude that some variables are integrated at level while some other variables are integrated at the first difference, so for testing the cointegration or long-run relations among these variable the panel cointegration tests are employed and these tests conclude the long-run association among the variables. For checking the long and short-run impact of institutional quality, FDI, domestic investment and economic growth the panel ARDL test is employed. The results demonstrate the significant positive long and short-run impact of institutional quality, FDI, domestic investment on economic growth in the region of South Asia. These relationships demonstrate the persistent nature of interactions among these variables, emphasizing the importance of policies that promote strong institutions, attract foreign investments, and stimulate domestic capital formation to sustain regional economic growth. The study offers critical recommendations for South Asian countries to enhance their economic growth by focusing on the strengthening institutional quality, encouraging FDI, developing the special economic zones, promoting domestic investment, investing in the infrastructure and promoting regional economic integration. These recommendations offer a clear path for South Asian countries to achieve sustained economic growth by addressing the interplay between institutional quality, FDI and domestic investment dynamics. By implementing these measures, South Asia can secure lasting prosperity and enhance the well-being of its people in a rapidly evolving global economic landscape.

TESTING THE MARKET EFFICIENCY WHEN INTEREST RATES CHANGE: CASE IN INDONESIA

Until the end of September 2023, the interest rate policy in the Republic of Indonesia shifted from 3.50% to 5.75%. The objective of this study is to examine whether changes in interest rates have an impact on the market efficiency in Indonesia. The daily market returns of Indonesia Stock Exchange (IDX) indices from January 3rd, 2022, until October 9th, 2023 are employed. To examine the market efficiency in weak form, this study conducts the normality test by the Kolmogorov-Smirnov test, the unit root problem test by Augmented Dickey-Fuller test, and the variance ratio test. This study finds that most market indices experienced a decline in returns and were less volatile when interest rates shifted to higher levels. This study also finds that most market indices tend to remain efficient in weak form at both low and high interest rates. The finding implies that those indices tend not to be influenced by policy changes in interest rates. However, the findings also show that several market indices experience changes in conditions when interest rates shift. As the studies on the relationship between changes in interest rates and market efficiency are scarce, the novelty of this study is the use of event windows to get specific impact of changes in interest rates on market efficiency.

Trade Horizons: A Comprehensive Study of Determinants Shaping Export Diversification in Pakistan

The present study aims at highlighting the determinants and degree diversification of export in Pakistan. The index of export’s diversification by the United Nations Conference on Trade and Development is used to calculate the degree of export diversification. Furthermore, using time series data from 1995 to 2022, present study examines the factors that influence export diversification. There are very few studies that address estimating and determinants in the context of Pakistan. To check the unit root problem Augmented Dickey Fuller test is used. The Auto Regressive Distributive Lag method is used in this study to observe long-term relationships in the underlying variables. The regression results indicate that official exchange rates, economic globalization, and foreign direct investment negatively affect export diversification in Pakistan. While, gross fixed capital formation, world GDP per capita, term of trade, and political stability positively affect diversification of exports in the case of Pakistan.

New insights from selected South Asian countries on the determinants of GHG emissions

In developing economies, there is a lack of certainty in energy and environmental economic policy. In contrast, research into the interplay between population, economic growth (GDP), renewable energy, nuclear energy, fossil fuels, and greenhouse gas (GHG) emissions has received comparatively little attention. Thus, the main motive of this exploration is to analyze the influence of population, economic growth, and various energy sources consumption on the selected South Asian economy's GHG emissions using the STIRPAT model. The period 1972–2021 is used to estimate results by considering cross-sectional dependence (CSD) and slope homogeneity (SH) tests, as well as second-generation unit root and cointegration tests. Due to the presence of SH, CSD, and mixed-order unit root problems, the cross-sectional autoregressive distributive lag (CS-ARDL) model has been used to estimate the value of selected variables. The impact of GDP and population is positive but insignificant for South Asian countries in the short run, but GDP is significant in the long run. The research also revealed that burning fossil fuels significantly contributes to atmospheric gas emissions level. In addition, renewable and nuclear energy play useful and substantial roles in reducing pollution in South Asian countries. Therefore, it is recommended that the economies of the South Asian countries maintain a uniform approach to economic policy to fully benefit from the advantages of increased green and safe energy production.

AN INVESTIGATION ON FACTORS AFFECTING HEALTH CARE EXPENDITURES: THE CASE OF PAKISTAN

In order to complete this study on factors affecting health care expenditures, we collected the annual data from 1972 to 2020 in order to assess the factors affecting health care expenses. The time series data frequently have unit root problem, which is present in data utilized in this study. It was scientifically proven by Eigen value test results that there is a causal relationship between health expenditures and the factors affecting those expenses. The Johansen co-integration approach has been used to examine long-term relationship between the variables. The study's conclusion is that access to health care in Pakistan is both a need and a luxury. Health care costs in Pakistan are being significantly impacted by urbanization. The number of doctors and hospital beds per 1000 people has a beneficial impact on health care costs. People under the age of ten and those who are older are more vulnerable and are having a favorable impact on health costs. The results offered the significant information in reaching the conclusion and offering suggestions to the policy makers and future researchers.

Examining the Impact of Economic Growth, Poverty and Unemployment on Inflation in Indonesia (2000-2019): Evidence from Error Correction Model

The study examines the causal relationship of economic growth, poverty, unemployment, and inflation in Indonesia from 2000 to 2019. The data analyzed are sourced from World Bank Indicators from The World Bank. The variables studied were inflation as the dependent variable and economic growth, the level of poverty, and the unemployment rate, inflation, as an independent variable. The results of the Augmented Dicky-Fuller test show that there is a unit root problem because all variables are not stationary at the level, so it is continued by doing first differencing, and stationary data is found in the first difference or I(1). Using the Vector Error Correction Model (VECM), this study found a significant long-term relationship between inflation (INF), economic growth (GP), the poverty rate (POV), and the unemployment rate (UNEMP). Meanwhile, in the short term, economic growth and poverty rates have a significant adverse effect on inflation. The results have important policy implications, specifically, the urgency of government policies and interventions to encourage the expansion of economic activities that promote economic growth and eradicate poverty.

Human Capital and Carbon Emissions: The Way forward Reducing Environmental Degradation

Many environmental problems are human induced, one of which is the change in atmospheric composition, a hot research topic in recent decades. This study aims to investigate the impact of human capital (HC) on carbon dioxide (CO2) emissions in Sri Lanka using time series annual data from 1978 to 2019. The time series data were examined for a unit root problem and an unknown structural break. An autoregressive distributed lag (ARDL) approach was employed to identify the long-run relationship between HC and CO2. The results confirm the long-term relationship between carbon emissions and human capital. As a unique finding of this research, the estimated coefficient of human capital to carbon emission is negative and statically significant, suggesting that a 1 percent increase in HC decreases carbon emissions by 1.627789 percent. The significance of this finding is that it can help achieve Sustainable Development Goal “13”, which focuses on combating climate change and its effects. The study indicated that building in HC by investing more in education helps to reduce carbon emissions in the long term. It reflects that human capital accumulation is linked to reduced environmental degradation due to lower CO2 emissions.

PUBLIC EXPENDITURE AND INFLATION RATE IN NIGERIA

This paper examined the relationship between inflation and government spending in Nigeria for the period 1990 to 2022. The study utilized Augmented Dickey-Fuller unit root test, Johansen cointegration; Granger causality Test and Vector Error Correction Model (VECM) approaches. The secondary data variables in consideration are government spending (GEXP), inflation rate (INF), exchange rate (EXR) and broad money supply (MS2) and they were soured from CBN Statistical Bulletins. Although the unit root tests showed that the variables possessed a unit root problem at their level but freed from this at their first order of integration, the Johansen co-integration result showed that the variables were co-integrated. The result indicated that there is a long-run and shortrun bilateral causal relationship between inflation and government spending. The regression estimate based on the short run and long run VECM showed that inflation rate has a positive significant influence on government spending in Nigeria over the study period. Money supply is positively related with government spending in the recent years. Meanwhile exchange rate over the study periods showed a significant reduction in government spending in Nigeria in the recent years because a rise in exchanger rate reduced the value of naira and hence affect the government expenditure negatively.

TCMB Ağırlıklı Ortalama Fonlama Maliyetinin Modellenmesi: LNV-ARMA Yaklaşımı

In this study, we model the monthly time series of the Central Bank of the Republic of Turkey’s Weighted Average Funding Cost (Interest Rate) for the period between 2011:01-2020:12. In this framework, we establish and compare the linear and the nonlinear based various autoregressive (integrated) moving average models in two separate groups and investigate the most suitable model for the series. After all, we reveal that the relevant interest rate series can be modelled best with the LNV-ARMA(2,1) model for the related period. The first novelty of this study is that we model the relevant interest rate itself instead of investigating the relationship of this interest rate with the other macroeconomic variables. The second novelty of this study is that we circumvent the unit root problem and establish a more explanatory time series model by applying the LNV methodology.