Panel Unit Root Tests Research Articles

The pathway to sustainable development: The roles of financial development, technological innovation, and educational development in energy poverty alleviation

AbstractThis paper investigates the dynamics of energy poverty by evaluating the roles played by financial development, technological innovation, and educational development, among other factors. It seeks to identify pathways for sustainable development in the Belt and Road Initiative (BRI) countries. Based on recent panel data from 2000 to 2020, this research classified the sample into various income groups for a comparative analysis. A multidimensional comprehensive index of energy poverty was then formulated through principal component analysis (PCA). This study assesses the relationships between variables using a novel panel quantile autoregressive distributed lag (QARDL), ordinary least squares (OLS), panel unit root tests, Kao cointegration tests, and Wald tests. The results of the Kao tests confirm the presence of cointegration among the variables across all income groups and the overall sample. The panel QARDL approach indicates a significant and negative long‐term relationship between financial development and energy poverty across all income groups other than the low‐income countries. Similarly, technological innovation has a negative long‐term association with energy poverty for most groups, with the exception being the upper‐to‐middle‐income countries. Furthermore, educational development negatively influences energy poverty in the long term for all groups other than lower‐middle‐income countries. The findings for the overall sample suggest that financial and educational development are negatively connected with energy poverty in both the long and short term, while technological innovation only has a negative connection in the short term. In addition, the Wald test results revealed time‐varying integration and parameter stability in both the long and short terms.

The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: An Extensive Simulation Study

The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.

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.

The nonlinear impact of renewable energy, fossil energy and CO2 emissions on human development index for the eight developing countries

This study analyzes the impact of renewable energy, fossil energy, CO2 emissions, urbanization, international trade and industrialization on human development for the eight developing countries over the period 1990–2021 using Karavias and Tzavalis (2014) panel unit root test regarding structural breaks and cross-section dependence and feasible generalized least squares (FGLS) estimator technique. The countries of interest are Brazil, Russia, India, China, South Africa (BRICS countries) plus Egypt, Iran and Turkey while human development index is used as a measure of human development. The findings reveal that there exists a U-shaped relationship between renewable energy consumption and HDI an inverted U-shaped relationship between fossil energy/CO2 emissions and HDI is relevant. Moreover, the positive impact of urbanization, international trade and industrialization on HDI is determined. As for policy implications, the policies oriented to renewable energy and to reduce fossil energy dependence and CO2 emissions should be adopted to improve human development in the developing countries. Additionally, policies promoting urbanization, international trade, and industrialization are necessary to achieve high levels of human development, provided that the negative effects of these policies are mitigated.

The Effects of Industrial Value Addition and Energy Consumption on Environmental Deterioration: New Evidence from Islamic Countries

The current research is aimed at finding out the effects of energy consumption and industrial value addition on environmental deterioration. Panel data for the years 2000-2017 was employed to explore the long- and short-term association of variables for the selected Islamic countries. Panel Unit Root Test was used to check the stationarity of the data. Moreover, Fisher panel Co-integration tests, PMG, Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square method (DOLS) were also applied to find out relationship between the variables. The study suggested that economic growth, industrial value addition and energy consumption positively affect the CO2 emission. Moreover, high-energy consumption to meet the demands of energy in transportation and production sectors leads to increased environmental pollution. The coefficient of industrial value addition shows significant effect on environmental deterioration in long term. Our study suggests the use of cleaner technology in production system and replacing renewable energy by non-renewable energy sources.

The relationship between transport infrastructure and output growth in rural nodal municipalities within South Africa

Transport infrastructure is an integral part of economic and social development in many countries, including South Africa. Economic growth theories state that a sound transport system enhances accessibility and output. Despite this, rural areas in South Africa face widespread poverty, poor infrastructure and limited access to essential services. This paper aims to examine the impact of transportation infrastructure investment on the output growth of rural nodal district municipalities in South Africa. This study focuses on two main objectives 1) investigating the relationship between transport infrastructure investment and output growth in rural nodal district municipalities and 2) understanding the causal relationship between transportation infrastructure investment and output growth in rural nodal district municipalities—this analysis uses panel data from 2012 to 2019. We used Fisher type (ADF and PP) panel unit root tests (Maddala & Wu,1999) to determine if the variables were stationary. Additionally, we employed Pedroni tests (1999) to ascertain the presence of cointegration among the variables. Once cointegration was confirmed, our study utilised the Panel VECM. A Panel Granger was then conducted to analyse the direction of causality between transportation infrastructure investment and output growth. This study recommends that the government should improve investments in transportation to improve the output of rural nodal district municipalities.

Corporate Social Responsibility, Earnings Management, and Cash Holding: A Study of Non-Financial Firms in Pakistan

This study investigates the interplay between CSR practices, earning management, and cash holdings in Pakistani publicly listed companies (PLCs). Spanning the period from 2013 to 2022, the research aims to provide empirical insights into how CSR initiatives influence financial practices within the Pakistani context. Methodologically, the study employs panel unit root tests and compares Ordinary Least Squares (OLS), fixed effects, and random effects models to analyze a sample of 202 non-financial firms listed on the Pakistan Stock Exchange. The implications of these findings underscore the importance of strategic CSR integration to enhance financial transparency, mitigate agency conflicts, and foster sustainable corporate growth. Managerial implications suggest that executives should focus on enhancing board effectiveness, strengthening audit oversight, aligning managerial incentives with long-term shareholder interests, and integrating CSR initiatives into core business strategies. However, the study acknowledges several limitations. These include the sector-specific focus on non-financial firms, which may limit generalizability to other sectors within the Pakistani economy. The study's reliance on panel unit root tests and econometric models, while robust, may benefit from alternative methodological approaches and sensitivity analyses. In conclusion, while this study contributes valuable insights into the CSR practices and financial management of Pakistani PLCs, future research should explore longitudinal trends, sector-specific variations, the role of emerging technologies in integration of CSR into corporate strategies. Addressing these areas would further enhance our understanding and inform policy interventions aimed at promoting transparency, accountability, and sustainable in emerging market economies like Pakistan.

HUMAN CAPITAL AND PROFITABILITY: CASE of TURKISH BANKING SECTOR

This study aims to examine the effect of human capital on profitability of Turkish banking sector by a panel data covering 2009.q1-2022.q4. Throughout this aim, a research model based on a dependent variable as return on equity is set up. The independent variable to be focused is the Human Capital Efficiency Coefficient -mostly considered as the most significant sub-coefficient of the Value-Added Intellectual Coefficient Methodology developed by Ante Pulic (2004)- to measure intellectual capital efficiency. Besides, three control variables are included in the model to proxy balance sheet structure, assets quality and liquidity. Levin-Lin-Chu (LLC), Im-Pesaran-Shin (IPS) and Augmented Dickey Fuller (ADF) Fisher panel unit root tests, and Pedroni (1999) panel co-integration test are conducted in the econometric analyses to test the stationarity and co-integration among the series. Long term co-integration coefficients are estimated by Fully Modified Ordinary Least Squares (FMOLS) methodology proposed by Pedroni (2000). The empirical findings indicate the existence of a statistically significant and positive relationship between human capital and profitability; and statistically significant and negative relationships among human capital and assets quality, and human capital and liquidity. Another finding is that there exists no statistically significant relationship between human capital and balance sheet structure.Levin-Lin-Chu (LLC), Im-Pesaran-Shin (IPS) and Augmented Dickey Fuller (ADF) Fisher panel unit root tests, and Pedroni (1999) panel co-integration test are conducted in the econometric analyses to test the stationarity and co-integration among the series. Long term co-integration coefficients are estimated by Fully Modified Ordinary Least Squares (FMOLS) methodology proposed by Pedroni (2000). The empirical findings indicate the existence of a statistically significant and positive relationship between human capital and profitability; and statistically significant and negative relationships among human capital and assets quality, and human capital and liquidity. Another finding is that there exists no statistically significant relationship between human capital and balance sheet structure.

Unveiling the synergy: Green finance, technological innovation, green energy, and carbon neutrality.

This study investigates the pivotal role of green strategies in achieving carbon neutrality by exploring the synergistic contributions of green finance, green technological innovation, and green energy adoption. The study has implemented several panel data estimation techniques including second generation panel unit root test commonly known as CADF and CIPS, an error correction-based panel cointegration test, for documenting the elasticities of GF, GTI, and GE on carbon neutrality through Continuously-Update Fully Modified[CUP-FM], Continuously-Update Bias-Corrected [CUP-BC], and Dynamic Seemingly Unrelated Regression [DSUR]. The asymmetric coefficients have exploded with the implementation of a nonlinear framework, which is well known as NARDL. Our findings underscore the significance of green finance mechanisms in mobilizing resources for sustainable initiatives, including renewable energy projects and energy-efficient technologies. Study shed light on the catalytic impact of green Technological innovation in driving technological advancements, reducing emissions, and fostering economic growth. Furthermore, our study delves into the transformative potential of clean energy adoption, elucidating how it can substantially reduce carbon footprints and bolster the transition to a low-carbon economy. This study contributes to the growing body of knowledge on the critical nexus of green strategies and carbon neutrality, offering a roadmap for a more sustainable and environmentally responsible future. In a world grappling with the pressing challenges of climate change, our research offers valuable insights into the strategies that institutions, policymakers, and businesses can employ to facilitate the transition toward carbon neutrality.

Trade‐offs and synergies: Examining the impact of natural resource rents and energy efficiency on financial development in the RCEP context

AbstractThis study investigates the complex interplay between financial and economic development within the Regional Comprehensive Economic Partnership (RCEP) countries—Australia, China, Indonesia, Japan, and South Korea—from 2000 to 2022, with a focus on the impacts of natural resource rents and energy efficiency. Employing moment quantile regression to address non‐normal data distribution identified by the Jarque–Bera (JB) test, we unveil diverse slopes and cross‐sectional interdependencies, necessitating a nuanced examination. Panel unit root tests confirm non‐stationarity, enabling a thorough investigation. Our findings reveal a long‐term cointegration relationship between natural resource rents, energy efficiency, economic development, and foreign direct investment (FDI), providing a holistic understanding of their dynamics. Notably, our analysis exposes the trade‐off between natural resource abundance and financial growth, as evidenced by negative coefficients for natural resource rents, while positive coefficients for economic growth, energy efficiency, and FDI counterbalance this effect, fostering a conducive financial environment. Additionally, our examination of location and scale characteristics unveils vulnerabilities associated with natural resources alongside stability indicators linked to economic growth, energy efficiency, and FDI. By employing quantile regression, our study sheds light on the nuanced impacts of natural resources, offering valuable insights for policymakers and researchers in navigating sustainable financial development within the RCEP framework.

Convergence of CO2 emissions among the selected countries on the Silk Road: evidence from nonlinear panel unit root tests.

This study analyzes the convergence of carbon dioxide (CO2) emissions by examining the stationarity of the relative per capita CO2 emissions of 18 selected countries on the Silk Road for the period 1990-2020. To examine the stationarity of relative per capita CO2 emissions for those 18 countries, we applied a large battery the newly proposed nonlinear panel unit root tests that allow for several forms of state-dependent and time-dependent nonlinearities. We also applied conventional linear panel unit root tests. The linear and nonlinear panel unit root tests account for cross-country dependencies, and the SPSM procedure is applied to these tests in order to see how many countries in the panel sample are converging to the steady state. The test results of linear and nonlinear panel unit root test reveal that the relative per-capita CO2 emissions of 10 out of 18 countries are stationary meaning that the CO2 emissions of these 10 countries converge to the steady-state level over time. Especially, size and sign nonlinearities better capture the convergence dynamics of per capita CO2 emissions towards the steady-state level for seven countries.As we have foundthat 56% of countries' per capita CO2 emissions are converging, this result has important policy implications.

Digitalization and economic development: lessons from globalized developing countries

Purpose This paper aims to empirically investigate the dynamic interlinkages among globalization, digitalization and economic development in the top 75 most globalized countries from 2000 to 2019. The selection of the 75 most globalized developing countries is based on the overall scores of the KOF Globalization Index (2021). Design/methodology/approach The research design is based on secondary data collected from the World Bank (2021), the International Telecommunication Union (2021) and the KOF Globalization Index (2021). The study uses panel unit root tests followed by the panel cointegration techniques. Further, the estimation uses panel fully modified ordinary least squares and panel dynamic ordinary least squares methods. Findings The empirical results reveal that the effect of globalization on economic development is sensitive to different estimation procedures; in some cases, but not in every case, the effect is positive and significant. However, the positive and significant effect of digitalization on economic development is robust across all estimated models. Long-run equilibrium relationships and bidirectional causalities strongly affirm the nexus among globalization, digitalization and economic development, substantiating the interconnectedness among 75 developing economies. Originality/value The study reinstates that the forces of globalization and digitalization will be instrumental in shaping the selected most globalized economies in the long run. Adopting various econometric methodologies takes care of the time-specific and cross-sectional dynamics, as evident in the panel framework considered in this study. The empirical findings truly ascertain the theoretical synergy among the forces of globalization leading to more digitalization and economic development. This makes the empirical interplay highly conducive to framing long-term policies to expand the information communication network in terms of its access and reach.

Effect of Financial Metrics on Risk Indicators in Nigeria Listed Deposit Money Banks

This study investigates the effect of financial metrics on risk indicators in Nigerian deposit money banks. The analysis employs yearly time series data spanning from 2007 to 2022, acquired from the Exchange Group PLC. Descriptive statistics, panel unit root tests, Hausman tests, and Panel Ordinary Least Squares (OLS) procedures were used at a 95% confidence interval. The study utilized secondary data sourced from the Exchange Group PLC database. The R-squared (0.564390) and Adjusted R-squared (0.540629) values indicate that the models have strong explanatory power. The results show that all variables are stationary at their levels (I(0)). The primary financial metrics influencing risk indicators among deposit money banks in Nigeria are revenue growth, net interest margin, and earnings per share. It was recommended that banks should implement effective risk management systems that can handle increased complexity and scale of operations, and regularly update them, leveraging blockchain technology for decentralized risk management as it relates to revenue growth rate of banks. Maintain a healthy net interest margin through effective risk management practices and internal controls, and utilize this strength to invest in risk mitigation measures, introducing incentive programs to encourage employee involvement in risk management. Conduct regular financial reviews and audits to ensure accurate earnings reporting and risk identification, utilizing AI-powered tools for earnings analysis to identify anomalies and potential risks. Prioritize prudent lending practices and effective risk management to maintain financial stability, implementing dynamic adjustments to the debt-to-equity ratio in response to changes in risk detection needs.

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.

Assessing Fiscal Sustainability with Panel Unit Root, Cointegration, and Granger Causality Tests: Evidence from the Broader Groups of Countries

Abstract The question of fiscal sustainability of countries has become one of the central topics in economic policy, especially in times of increasing public debts. One way to assess fiscal sustainability is to examine compliance with the intertemporal budget constraint, which involves testing the stationarity of government revenues and expenditures, the primary budget balance, and the first differences of public debt. Part of this approach includes testing cointegration and causality among different pairs of variables. Under this approach, the paper is focused on both first- and second-generation panel unit root tests, cointegration, and Granger causality test. This paper focuses on assessing the fiscal sustainability of four panels of countries divided by continents: Europe, Asia and Oceania, Africa, and Latin America, the Caribbean, and North America. It has been found that fiscal sustainability is present in all the broader groups of countries considered but in a weak form. Fiscal sustainability was confirmed when considering a constant in calculations, while deviations were observed among groups of countries when both a constant and trend were considered. The study serves as a starting point for a more extensive analysis of fiscal sustainability. For more accurate findings, it would be necessary to categorize countries into smaller and economically more homogeneous groups and analyze them using other fiscal sustainability methods as well.

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.

Study on stochastic convergence of China’s carbon emission efficiency

ABSTRACT Carbon emission efficiency (CEE) and its stochastic convergence are important indicators for achieving the strategic goal of carbon peaking and carbon neutrality. Thus, this paper firstly estimates the CEE of 30 provinces in China from 1995 to 2019 with the super efficiency of the SBM model. Then the panel unit root test method is adopted to test its stochastic convergence. The results show that the overall CEE has not reached the effective level, and the CEE level varies greatly among regions. There is a stochastic divergence of CEE in most regions from 1995 to 2001. While the CEE of most regions changes to convergence after 2002. From 1995 to 2019, the CEE of the western, northeast, and southwest regions diverge. While the CEE of the national level, the eastern, central regions, and the other six regions in the eight major economic zones converged stochastically. Among the divergent areas, the CEE of Heilongjiang, Guangxi, and Sichuan provinces are the main contributors to the CEE divergence. Therefore, it is the key to narrowing the differences in regional low-carbon development in China by significantly improving the CEE of each province.

The role of financial development, natural resources, and technology in determining green energy: New findings from the N‐11 countries

AbstractIn recent years, there has been a growing global emphasis on transitioning to renewable energy sources to mitigate the effects of climate change and promote sustainable development. The role of financial development, natural resources, and technology in determining the advancement of green energy has been a subject of ongoing research. This study examines the impact of financial development, natural resources, and technology on green energy development in N‐11 countries between 1990 and 2022, using economic growth and education as control variables. The empirical estimations are based on cross‐section dependence, testing for slope heterogeneity, second‐generation panel unit root tests, LM‐Bootstrap cointegration tests, fully modified ordinary least squares, and dynamic ordinary least squares. A 1% increase in natural resources is associated with a 0.0615% increase in the adoption of green energy. A 1% increase in education leads to a statistically significant increase of 1.0734% in the adoption of green energy financial development enables the deployment of renewable energy sources and access to financial resources. Natural resources determine the types of renewable energy technologies that are prioritized. Technological advancements drive the growth of renewable energy technologies and facilitate their integration into the energy mix. By analyzing the findings of recent studies on the N‐11 countries, we can gain valuable insights into the importance of these factors in advancing green energy and achieving sustainable energy development.

Does Political Stability Matter to the Profitability of Banks?

This study examines the impact of net interest margin, non-interest income, non-performing loans, and Political Stability Index on the banks’ profitability regarding return on assets in developing Asian countries, focusing on how political stability affects the bank’s profitability. The secondary data from 14 developing countries used in this study are collected from the World Bank and World Financial Studies Report. The dataset includes 154 data points spanning from 2012 to 2022, focusing on 14 underdeveloped countries of Asia. Exploratory and analytical research designs are utilized. Data are analyzed by using EViews 12. Econometric tools such as descriptive statistics, correlation analysis, panel unit root testing, Fisher-Johnsen combined co-integration test, panel vector error correction model, and Wald test investigate the relationship between response and predictor variables. The net interest margin, non-interest income, non-performing loan, and Political Stability Index are jointly integrated to determine the bank’s profit of developing countries of the Asia continent. Interest margin, non-interest revenue, non-performing loans, and the Political Stability Index all show a 38.6 percent variance in bank profit. It has been discovered that for every unit that rises in net interest margin and Political Stability Index, the bank profitability of developing Asian countries increases by 0.4867 and 0.2221 percent, respectively. Non-interest income has little bearing on the profitability of banks. Meanwhile, non-interest income exhibits minimal influence, suggesting a need for a strategic focus on interest margin enhancement and fostering political stability to optimize banking sector profitability in the region.

Exploring the Impact of Remittance and Economic Growth on Inflation

This study investigates the influence of remittance income and economic growth on the inflation rate in South Asian Association for Regional Corporation (SAARC) countries. Remittance inflows have a double effect on the economy’s inflation rate: firstly, it increases consumer spending capacity, driving up demand for goods and services, which can lead to higher prices; secondly the influx of foreign currency can cause local currency appreciation, raising import costs and contributing to inflation. While previous studies have focused primarily on how inflation hinders economic growth, this study seeks to understand how economic growth causes inflation from the other side. Economic growth boosts consumer and business spending, increasing demand for goods and services, which can increase prices. Furthermore, higher demand for resources and labor can raise production costs, further contributing to inflation. The correlation between remittance income and inflation has been extensively documented, but the impact of economic growth on inflation remains to be determined. The study is based on descriptive and exploratory research design and driven by a positivist research philosophy. The present study is based on secondary data collected from World Bank reports and the countries’ economic surveys. This study uses an unbalanced panel dataset of 272 data points from eight member countries of SAARC. Basic econometric techniques such as covariance analysis, panel unit root testing, panel autoregressive distributed lag model, and scaled coefficient analysis are employed. The remittance income is only statistically significant at the 10 percent significance level. A one percent increase in remittance income leads to a 0.284 percent increase in inflation over the long run. The growth rate of gross domestic product plays a crucial role in setting inflation. One percent rise in gross domestic product growth leads to a 0.3405 percent increase in inflation in the long term. The error correction term exhibits a negative and statistically significant relationship. The coefficient size suggested that around 65.14 percent of the disequilibrium was rectified in each cycle. The current year’s lagged gross domestic product growth negatively and statistically significantly impacts economic growth. One percent rise in gross domestic product growth led to 0.3308 percent fall in inflation in the short term across SAARC countries. The results indicate that remittance income has a minimal long-term effect on inflation in SAARC nations, whereas gross domestic product growth substantially impacts inflation. Policymakers should prioritize promoting sustainable economic growth to mitigate inflationary pressures effectively.