Econometric Tools Research Articles

Eco-tech fusion: Unraveling the nonparametric causal effects of fintech, natural resources, digital infrastructure, and economic growth on environmental sustainability from a quantile perspective

Environmental sustainability has the potential to uphold an ecological balance in the natural environment. Unfortunately, it is challenging due to the environment's multifaceted nature and the complex interlinkages of environment with social, economic, and technology-related factors. The current study inspects the causal effect of fintech, natural resource rent, digital infrastructure, and economic growth in relation to environmental sustainability. This study comprises the time frame of 2002–2022 in the BRICS nations. The "nonparametric causality-in quantiles approach" has been applied, which is an advanced econometric tool. The results confirm that all BRICS countries, i.e., Brazil, Russia, India, China, and South Africa, show a significant causal impact of the said predictors on sustainable environment in most cases. This study is significant for scholars, investors, policymakers, manufacturers, and environmental regulatory bodies.

Анализ демографических показателей в Тульском регионе: проблемы и перспективы

The article analyzes the criteria affecting the level of demographic indicators in the Tula region. A comprehensive study of the birth rate in the Tula region for the period from 2002 to 2022 was carried out, using econometric tools, factors influencing the birth rate were selected, a regression model was built, and a forecast was made for the development of the birth rate in the Tula region. The result of the study was the identification and assessment of the significance of factors influencing the birth rate in the Tula region.

Investigating the determinants of marine environmental degradation in Africa: What roles can economy, population, capture and energy play?

This study firstly examines the quality of marine eco-environment in Africa using Tapio decoupling model, and analyzes the sustainability level of the development of “population agglomeration - marine environment - economic growth”. Secondly, a series of econometric tools, such as ARDL, FMOLS, AMG model and DH panel causality test, are used to investigate the long- and short-term impacts of economic growth, population agglomeration, marine capture and energy consumption on the African marine eco-environment, and to analyze the differences between the sub-regions in Africa. The results indicate that: Adebayo and Kirikkaleli (2021) (Adebayo and Kirikkaleli, 2021) the decoupling state of “population-environment” has shifted from expansive negative decoupling to more optimized strong decoupling, and “economy-environment” has gradually changed from strong negative decoupling and expansive negative decoupling to strong decoupling. Ali et al. (2017) (Ali et al., 2017) There existed a bi-directional causal relationship between the degree of marine environment degradation and economic growth, population agglomeration, marine capture and energy consumption. Al-Mulali and Sab (2012) (Al-Mulali and Sab, 2012) In the short term, the economic EKC hypothesis does not hold in North and West Africa, while Central, East and Southern Africa are consistent with the EKC hypothesis. In the long term, the EKC hypothesis is valid in Central, East and Southern Africa, while is not valid in North and West Africa. Overall, reducing population agglomeration levels, marine fishing and energy consumption might mitigate marine environmental degradation in Africa.

Examining the impact of climate change on cereal production in India: Empirical evidence from ARDL modelling approach

Agriculture sector is major sufferer of climate change both at a global level as well as at India level. Cereals account for about 92 % of India's total food grain output and climate change has a significant influence on the production of cereals. This study aimed to evaluate the long-term and short-term effects of climatic and non-climatic variables, specifically temperature, precipitation, cereal area, total cropped area, fertilizer consumption, and pesticide consumption, on cereal production in India. The study included annual time series data that covered the period from 1960 to 2018, covering a period of 58 years. Various econometric techniques were employed to examine these relationships. The validity of a long-term and short-term relationship among the relevant variables included in the study was validated by employing the Autoregressive Distributed Lag (ARDL) technique and the Johansen cointegration test. The ARDL model's estimation outcomes reveals that input factors such as cereal area became a key factor in rising cereal production, as evidenced by its positive coefficient. Similarly, fertilizer consumption and precipitation had positive effects on production in the long run whereas total cropped area and minimum temperature has little influence over the results of production both in short run as well as long run. Furthermore, the long-term findings were also supported using econometric tools like Canonical Cointegrating Regression (CCR) and Fully Modified Least Squares (FMOLS). These methods confirmed that variations in cereal production in India were significantly influenced by both climatic factors and agricultural inputs and factors. The study emphasizes the urgency for policymakers to prioritize proactive measures aimed at reducing the adverse impacts of climate change on cereal production in India. This necessitates a comprehensive strategy integrating sustainable practices, technological innovations, and robust policy frameworks to ensure resilient agricultural sectors and sustainable food production.

Coffee in Indian Economy: Performance and Prospects

Coffee is a universal hot beverage, and its international trade volumes point to significant approval in favour of coffee consumption. For many nations in the tropics, coffee is a major source of foreign exchange. This study provides a comprehensive analysis of the global and domestic coffee industry, with a particular focus on India's role based on the secondary data taken from different sources like ITC trade map, Coffee Board of India and international coffee organization statistics, etc. In 2022, global coffee production reached 10.89 million tons, with Brazil, Vietnam, and Indonesia leading the output. Despite being the eighth-largest coffee producer, India's export ranking is relatively low due to significant domestic consumption. This study employs standard econometric tools, such as compound annual growth rate, Cuddy Della Index, Markov chain analysis, etc. The Johansen co-integration test revealed a long-run equilibrium relationship among selected Arabica and Robusta coffee markets. Furthermore, the pair-wise Granger causality test identified unidirectional causality in price transmission among certain Arabica markets, while bidirectional causality was observed among Robusta markets. The paper also highlights the declining coffee yield in India due to biotic and abiotic stressors, despite an increase in production and cultivation area. The study concludes with a discussion on the dynamics of Indian coffee exports, where high domestic demand and fluctuating international prices contribute to the sector's variability. This research provides valuable insights into the performance and prospects of the coffee industry within the Indian economy, offering a basis for future policy and market interventions.

Revisiting the economic growth in the shadow of financial stress in times of crisis: evidence from FIGARCH and wavelet coherence approach

PurposeThis study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.Design/methodology/approachTo address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.FindingsFinancial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.Originality/valueThe study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

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.

The consumption of natural resources and its effects on environmental quality: Evidence from the OECD countries

The existing body of literature has extensively investigated and debated the pivotal role of natural resources in the dynamics of economic growth. However, the explicit impact of natural resources on the quality of the environment is yet to be explored comprehensively. Amid this backdrop, this study explores the significant role that natural resources play in the growth processes of OECD member economies and the potential impact on environmental management. For our research model, the data for the OECD member countries (1997–2021) was collected from the “World Development Indicators (WDI)”. We applied econometric tools namely, fixed effects, generalized least squares and two stages least squares for empirical results. A causality exercise based on the Dumitrescu and Hurlin (DH) is conducted to identify the direction causation. Our results show that the use of natural resources adversely impacts the quality of the environment in OECD economies. The square term of natural resources is negative and significant, confirming the validity of the environmental Kuznets curve (EKC) hypothesis between the use of natural resources and environmental degradation. When the cubic term of natural resources is entered into the estimated model positively, it shows that the potential shape of the EKC hypothesis is N-shaped. Our results also demonstrate that energy consumption and urbanization have worsened environmental quality, while trade openness and per capita income have improved the quality of the environment in OECD countries. The causality analysis showed several one-way as well as two-ways relationships. Our study's findings provide valuable insights from the OECD economies, which can be used to design and execute policies for managing natural resources to address environmental problems.

Public Debt and Economic Growth in MENA Countries. An Analysis by Panel Co-Integration Techniques

This study analyzes the dynamic relationship between public debt and economic growth in the economies of the MENA region during 1996 -2020. To do this, two econometric tools were applied. The first method presents panel cointegration techniques and Granger causality tests to verify the existence of a long-term relationship and examine the direction of causality between the different variables chosen. The FMOLS and DOLS panel techniques were used to estimate the long-term parameters. Thus, we show that the long-term impact of public debt on economic growth is both positive and significant. The second method describes the link using a linear growth model and another dynamic model. The latter two were estimated using the generalized moments method with dynamic panel data. The results show a close relationship between the exogenous variable debt and endogenous variable economic growth, and that debt positively affects growth up to a certain threshold. Beyond this threshold, the effect becomes negative and significant. Nevertheless, we have concluded that from a certain threshold, debt can exceed the repayment capacities and, therefore, disadvantage growth, which will in turn discourage domestic investments and savings.

Impact of Inflation Rate, Remittance Income, and Public Loan on Government Expenditure: Evidence Derived from the Nepalese Economy

This study examines the impact of inflation rate, remittance income, and public borrowing on the government's annual expenditure. This study is based on the descriptive and exploratory research design. It follows the positivist research philosophy and quantitative characteristics. It uses secondary data collected from various economic surveys in Nepal and reports from the World Bank. It covers 33 data points from 1990 to 2022. Simple statistical and econometric tools like descriptive statistics, robust regression analysis, Normality test, and confidence interval test are used in this study. The inflation rate, public borrowing, and remittance income are responsible for increasing Nepal's government expenditure. The inflation rate is not individually significant in determining the spending in Nepal. Remittance income has a considerable positive impact on total expenditure in Nepal. One unit increase in remittance income results in a 0.109648 unit increase in government expenditure in Nepal. Likewise, public borrowing is highly significant in explaining government expenditure. One unit increase in public borrowing results in a 1.145037 unit increase in government expenditure in Nepal. Nearly 73.69 percent variation in total government expenditure depends on Nepal's inflation rate, remittance income, and public borrowing. Policymakers in Nepal should focus on bolstering remittance inflows and implementing prudent public borrowing management strategies to foster sustainable economic growth while recognizing the limited influence of the inflation rate on government expenditure.

The scientific tale of the nexus between oil prices, macroeconomic uncertainty and Pakistan's exports to its major trading partners: Insights from advanced methods

AbstractThe recent study aims to analyze the nonlinear dynamic effects of oil price shocks and macroeconomic uncertainty on exports. To achieve this, the study utilizes monthly data from July 2003 to December 2020 on oil prices and macroeconomic uncertainty, examining their impact on Pakistan's exports to its major trading partners. To ensure detailed and robust findings, the study employs various advanced econometric tools, including quantile unit root, cointegration, Granger causality tests, and quantile‐on‐quantile regression (QQR) and wavelet quantile correlation (WQC) techniques. The QQR and WQC estimates reveal diverse and nonlinear effects of oil price shocks and macroeconomic uncertainty on exports, reflecting the complexity of the relationship. While oil price shocks (OP) predominantly hinder exports in most cases, a significant and positive association between OP and exports is also observed. Similarly, macroeconomic uncertainty generally exhibits a significantly adverse influence on exports, but positive impacts are also evident. Furthermore, the quantile Granger causality test confirms the presence of a bidirectional causal relationship between the selected series. Based on the results mentioned above, the study argues that the effects of oil price shocks and economic uncertainty are nonlinear, diverse, and complex. As a result, the study suggests implementing phase‐wise policy recommendations to address these complexities.

IS THE PHILLIPS CURVE USEFUL FOR FORECASTING INFLATION IN RUSSIA?

The paper analyzes the experience of using the Phillips curve to model inflation in Russia, taking into account the specific features of domestic labor market. Based on Russian data for the period from 2000 to 2022, a wide range of inflation forecasting models has been built: both based on the Phillips curve and alternative ones. The econometric tools used are autoregression models with a moving average in the residuals taking into account seasonality (SARIMA) and their generalizations; autoregression models of distributed lags (ADL) and their generalizations; as well as other estimation methods. During the simulation, we use data on inflation, inflation expectations, production dynamics, unemployment, wages, exchange rates, money supply and other variables. The models are compared drawing on the accuracy of single-period and multi-period out-of-sample forecasts. The modelling results allows us to conclude that onedimensional models work well during the periods of stable economic dynamics, but lose in their predictive power to the “triangular” Phillips curve in crisis years. Comparison of models for forecasting inflation shows that in a stable economic situation, one-dimensional models provide a more reliable forecast. However, in the context of structural transformation faced by the Russian economy in 2022, the “triangular” models of the Phillips curve demonstrate maximum quality of the forecast. Although the acceleration of inflation in 2022 obviously reduces the accuracy of any forecast equations, however, the “triangular” model based on the lags in inflation, unemployment and the index of industrial production demonstrates the best results. This conclusion remains stable to changes in the length of the time series used for forecasting, as well as to changes in forecasting horizon.

Inequality and spatial mismatch in the urban labor market: Evidence for the Curitiba metropolitan region, Brazil

Accessibility plays a fundamental role in improving urban economic equity, especially for disadvantaged communities. In many metropolitan areas of Brazil, accessibility relies on infrastructure unequally distributed, creating barriers to access opportunities in the urban labor market. This paper aims to identify sources of spatial mismatch and measure its effects on the labor market of the Curitiba Metropolitan Region (CMR), in Brazil. First, we measured the spatial imbalances of households and jobs by income classes and their effects on the spatial dissimilarity of accessibility to job opportunities. Second, we use econometric tools to evaluate how accessibility to the formal job sector affects individual earnings in the CMR labor market by wage quartile. We find evidence of strong spatial segregation for the poorest individuals from public transit network and from the formal job sector. The econometric results show there was roughly no accessibility premium for individual earnings for the poorest workers and positive effects for the richest workers in the CMR labor market. This set of empirical evidence suggest a spatial concentration of urban amenities that enhance economic inequality for the CMR.

Exploring the Impact of Women Governance on CO2 Emissions in the European Union and Central Asia

The study is novel to contribute to the literature investigating the relationship among CO2 emissions, women governance, economic development, fossil fuel energy consumption and renewable energy consumption, using a balanced panel dataset of 27 European Union and 4 Central Asian countries over the period 1996-2020. As econometric tools, panel quantile and threshold regression models are employed. Overall, the quantile results document that women governance help to mitigate climate change both in European Union and Central Asia. Moreover, threshold findings suggest that women governance negatively impact on CO2 emissions in European Union and Central Asia when economic growth is higher than 9.903 percent. Policy implications are proposed to enhance women governance in European Union and Central Asia.

The Volatility Spillover of Global Oil Price Uncertainty

This manuscript, for the first time, analyses the volatility spillover of oil price uncertainty in the world using data from oil price uncertainty recently developed by Abdul and Qureshi (2023), spanning the time 1996-2019 on a monthly frequency. ARCH/GARCH (Autoregressive Conditional Heteroskedasticity and Generalized Autoregressive Conditional Heteroskedasticity) models are employed as an econometric tool. The findings suggest that ARCH model is more consistent than GARCH model in assessing the volatility of oil price uncertainty in the world. The results show that the volatility of oil price uncertainty is high in the world. The transition to renewable energy sources is proposed as a way to resist unexpected oil shocks since the production of renewables does not depend on the fluctuations of oil prices. Consequently, uncertainties in the oil price do not hinder economic activities.

Impact of Value Added Tax on Indirect Tax and Total Tax Revenue: An Empirical Study from Nepal

This study examines the impact of value-added tax on indirect tax and total tax revenue collection in Nepal. The secondary data that covers 48 annual data points collected from the various economic surveys are used in this study. Descriptive and exploratory research designs are used. Some statistical and econometric tools like graphs, scatter plots, regression analysis, variance analysis, multicollinearity test, normality test (Shapiro-Wilk), and heteroscedasticity test are used. VAT revenue is statistically significant in determining Indirect tax and total tax revenue in Nepal. One percent increase in value-added tax results in 0.919 and 0.944 percent increase in indirect tax and total tax revenue in Nepal. A variation of 98.9 percent in indirect tax and 97.8 percent in total tax revenue depends upon VAT in the Nepalese economy. Policymakers in Nepal might use these findings to make informed decisions regarding VAT rates and policies, recognizing the substantial influence of VAT on overall tax revenue. Int. J. Soc. Sc. Manage. Vol. 11, Issue-(1-2): 27-33.

The Construction of Economic Education Model Based on the Spatial Durbin Model

This research attempts to address the complexities and the nuances that are a part of the economic education by proposing an innovative model that is grounded in the Spatial Durbin Model (SDM). The spatial interdependencies which are crucial for the understanding of the regional variations in the economic implications and the efficacy of the educational policies are sometimes neglected by the conventional main frames of the economic education. The integration of the SDM, which is a sophisticated spatial econometric tool, aims to offer a thorough and detailed understanding of the intricate relationships between the education and the economic development in context of the diverse geographics. The model that is proposed in this research study extends beyond the traditional linear regressions as it accounts for the spatial autocorrelation and the spatial lag effects which thereby captures the spatial spillover and the diffusion of the educational investments and policies. With the help of the empirical analysis which moves forward by the utilization of the spatial econometric techniques such as the spatial lag and the spatial error models, strives to explore the spatial dynamics of the economic education while identifying the spatial patterns, the dependencies involved and the disparities that come up in the attainment of the educational and the economic performance. By incorporation of the spatial dimensions into the economic education model, the policymakers and the educators are able to get their hands on the in depth insights. This is into the spatially differentiated impacts of the educational interventions which tends to enable a more targeted and a more effective design and implementation of the policy. Furthermore, the proposed model also aims to facilitate the identification of the spatially targeted strategies in order to address the regional disparities with respect to the educational outcomes and the economic growth. Hence, it contributes to a more inclusive and a sustainable development trajectory. Through the interdisciplinary effort of bridging the economics, education and the spatial analysis, this research extends a valuable main frame for the policymakers, educators and the researchers which helps in attaining better understanding and helps to address the multifaceted challenges as well as the opportunities in the economic education and the regional development. Ultimately, this has enhanced the understanding of the spatial dimensions of the economic education which leads to the evidence based policies and interventions which are aimed at nurturing the equitable and resilient economic growth. In this paper, we utilize the spatial Durbin model as a framework to investigate the role that advanced degrees play in driving technological innovation in various parts of the world. Spatial self-correlation and instability in the geographic distribution of post-graduates in China were shown by data from the regional panel in China between 2004 and 2018. Advanced degrees facilitated the advancement of cutting-edge technologies.

Relationship between Socially Sustainable Supply Chains and United Nations Sustainable Development Goals: A Perspective from the Indian Auto Ancillary Industry

This article aims to understand the level of awareness about the United Nations sustainable development goals among supply chain leaders. Further, it explores how organizations’ social sustainability policies can help achieve sustainable development goals. The multi-stage process involves a structured survey of supply chain management leaders from the auto ancillary industry of the National Capital Region of India, and data were analyzed using econometric tools for meaningful insights. The significant findings from the study highlight that there is a considerable lack of awareness among supply chain leaders about the sustainable development goals defined by the United Nations. However, supply chain leaders make policies that influence social sustainability positively; they do not link it with sustainable development goals. The study reveals that leaders were unanimous on improving and adopting socially sustainable measures for creating efficient supply chains. This is one of the first studies that investigated the relationships between sustainable supply chain management and sustainable development goals laid down by the United Nations.

Nexus between Inflation, Economic Growth and Government Expenditure of Nepal

In the context of Nepal, this study will explore the relationship between the rate of inflation, economic growth, and government spending. Government spending has been broken down in this analysis into three categories: government recurrent expenditure, government capital expenditure, and government financing expenditure. The time series data from 1974 to 2021 are used in the investigation. The econometrics tools used to analyze this link are the Granger-causality test, ARDL, Johansen co-integration, and the Augmented Dickey- Fuller (ADF) unit root test. The outcomes of using these econometric techniques reveal a long-term relationship between the rate of inflation, economic growth, and government spending, which indicates that public spending has beneficial externalities and links. In the short run, government spending has a greater impact on economic growth than the rate of inflation. The results of the causality test indicate that there is a one-way causal relationship between the rate of inflation and both economic growth and government spending.

Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

PurposeThis study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.Design/methodology/approachSeveral econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.FindingsThe results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.Research limitations/implicationsThis study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.