Econometric Tools Research Articles

Sustainable practices in business operations - Merely a moral obligation of modern companies or a strategic asset for enhancing shareholders value.

Sustainable practices are critical to the operation of businesses. The realization that operating in an ecologically and socially responsible manner is the only way for a company to be commercially successful is spreading among more and more businesses. Using econometric tools, this paper examines the complex interactions between Environmental, Social and Governance (ESG) disclosure scores and firm performance metrices, such as ROA, ROE, and Tobin's Q. The research employs GMM and panel quantile regression to address the heterogeneous impact of ESG disclosure at different levels of performance. The results indicate that ESG and its components in the environmental and governance scores have varying effects, often with a negative impact for less successful firms but mixed outcomes for more successful firms. The innovative integration of panel quantile regression in this study elucidates how asymmetric effects of ESG disclosure influence firm performance-nuances not captured by standard methods. These findings have significant implications for corporate decision-making, regulatory policies, and stakeholder engagement in the context of sustainability practices.

Analyzing Shock Transmission and Spillover Effect in the Day-Ahead and Intraday Markets: Key Implications for Price Forecasting

Abstract The increasing capacity in renewables (RES) and batteries in the Romanian power system stirred discussions on increasing interdependency between day-ahead market (DAM) and intraday continuous (IDC) market. In this paper, we analyze hourly market-cleared prices and selling and buying quantities in the DAM and IDC over 3 months (4th of June to 11th of September 2024) to verify the connectedness between prices of the two markets, possible shock transmission and spillover effects. We employ generalized autoregressive conditional heteroskedasticity (GARCH) models to examine volatilities. The Diebold–Yilmaz spillover index is 95.69%, demonstrating an extremely high degree of volatility interdependence. This suggests that nearly all volatility in both markets is driven by cross-market shocks. Price shocks in the DAM initially raise IDC prices by 0.3 units, with this effect fading after 8 to 10 periods. Conversely, IDC shocks similarly raise DAM prices by 0.3 units, but both effects are temporary, indicating a short-term dynamic link between the two markets. Moreover, numerical results from the BEKK-GARCH model highlight significant volatility spillovers between Price_DAM and Price_IDC, with stronger cross-market volatility effects and evidence of interdependence, particularly from Price_IDC to Price_DAM. The findings suggest implications for market players to create bidding strategies and decision-makers to mitigate price volatility connectedness in DAM and IDC. Furthermore, our findings underscore the critical role of knowledge creation and diffusion in understanding the interconnected dynamics of electricity markets. By leveraging advanced econometric tools and spillover analysis, our research contributes to the body of knowledge essential for innovation in market strategies and decision-making processes.

International accounting as a research area: Searching for statutory guidelines

Subject. This article discusses the issues related to international accountingas an area of scientific research. Objectives. The article aims to show that at present there is no clear understanding of what international accounting is and why research in this area is needed. Methods. The research is speculative. For the study, I used the dialectical method of scientific knowledge, the method of collecting theoretical and regulatory information, the method of formalization, as well as analysis, synthesis, observation, and comparison. Results. The article defines two visions for the object of international accounting, namely narrow and broad ones, as well as the subject of international accounting. It presents two formalized models: the relationship between the quality of accounting information and the efficiency of capital allocation, and the relationship between the quality of accounting information and the average market value of capital. Using these models as an example, the article shows that modern research does not provide any convincing arguments in favor of the fact that the use of IFRS leads to an improvement in the efficiency of capital allocation in the economy. This problem is a promising area for the development of the research topic. Conclusions and Relevance. In the second half of the 20th century and up to the present, a narrow view dominates in the works in the field of international accounting. The broad view does not resonate among researchers, and therefore many important questions remain unanswered. In many works of researchers, econometrics tools are used to prove (disprove) a particular hypothesis, but there are no regulatory guidelines. The results of the work may be useful to a limited group of specialists in the field of international accounting, institutional theory and financial theory.

Impact of Economic Growth, Inflation, Government Effectiveness, and Corruption Control on Gross Capital Formation of Nepal

This study aims to investigate the impact of inflation rate, economic growth, effectiveness of government, and corruption control on gross capital formation in Nepal. Secondary data, which are collected from the World Bank Report and Nepal's economic surveys, is used in the study. The descriptive and correlational research designs are used to analyze the impact of predictor variables. This study is guided by positivist research philosophy and is mainly quantitative. To investigate the effects and validate the model, econometric tools include descriptive statistics, correlation analysis, dynamic ordinary least squares (DOLS), Phillips-Perron unit root test, and variance inflation factor. Other measures of residual testing include the Hansen parameter instability test, normality test, residual and standardized residual graph, and actual fitted residual graph. The effectiveness of government has a high degree (r=0.722) of positive association with gross capital formation. Economic growth, corruption control, the effectiveness of government, inflation rate, and gross capital formation have long-run co integration. Economic growth has a positive and significant impact on gross capital formation. One unit increase in economic growth results in a 1.4594 unit increase in gross capital formation in Nepal. The government's effectiveness also positively and significantly impacts gross capital formation in Nepal. One unit increase in the effectiveness of the government results from 0.8125 unit increase in capital formation in Nepal. The inflation rate has no significant impact on gross capital formation in Nepal. But surprisingly, corruption control negatively and significantly impacts capital formation. One unit change in corruption control resulted in a 0.8217 unit decrease in capital formation. About 97.57 percent variation in capital formation is determined by economic growth, effectiveness of government, corruption control, and inflation rate in Nepal. Policymakers should focus on enhancing economic growth through infrastructure, education, and business environment improvements, ensuring effective governance and stable inflation, and fine-tuning anti-corruption strategies to support capital formation in Nepal.

Оценка значимых факторов экономического развития муниципальных образований региона: пространственный анализ (на примере Ростовской области и Краснодарского края)

The study aims to identify the significant factors and their direct and external effects (externalities) that influence the economic productivity of municipal entities in the Rostov Region and Krasnodar Krai. The principal tasks focus on characterizing the economic space and delineating territorial clusters of accelerated development alongside zones of economic decline in the specified regions. A spatial econometric approach has been applied. For the purposes of this analysis, authors designed SAR (spatial autoregressive model) and SEM (spatial error model) models based on municipal statistics data for 2023. Model calculations revealed that the common significant factors influencing economic productivity in both Rostov Region and Krasnodar Krai include the volume of investments in fixed capital, the average monthly wage of employees in organizations, and the density of paved local public roads. Moreover, for Rostov region, an additional growth factor at the municipal level of municipalities is the average number of employees of organizations. Using the tools of spatial econometrics, the presence of a negative spatial autocorrelation of economic dynamics between neighboring municipalities of the region has been proved. Furthermore, for both subjects, based on calculations of local spatial aggregation indicators (LISA), territorial clusters of advanced development and zones of economic recession were identified. The results obtained can serve as a basis for developing a differentiated approach to determining the directions and tools for managing socio-economic development at the subregional level.

Agri-Tech startups transforming vegetable supply chains in Coimbatore

This research investigates the transformative role of digital agri-tech startups in optimizing vegetable supply chains in Coimbatore District, Tamil Nadu, India, with an emphasis on improving agricultural productivity and farmer welfare. Despite a marked 45% decline in agri-tech sector investments between the financial years 2021-2022 and 2022-2023, attributed to global interest rate hikes and economic uncertainties, startups such as WayCool, Farmers Fresh Zone and Farm again have demonstrated significant potential in enhancing supply chain efficiency and supporting farmer livelihoods through technological innovations. Employing a mixed-methods approach, this study integrates both qualitative and quantitative data derived from surveys, field visits, and interviews. Key performance indicators analyzed include Price Spread, Marketing Efficiency, Cost-Benefit Ratio and Productivity. Advanced econometric tools, including logistic regression and propensity score matching, were used to assess the impact of technology adoption on farmers’ outcomes. The results reveal that farmers utilizing digital platforms for crops such as onion and tomato achieved higher market prices, improved marketing efficiency and superior financial performance compared to those employing the conventional methods. Despite persisting challenges like resistance to technology adoption and inadequate internet connectivity, the findings emphasize the potential for substantial growth in the sector, driven by technological innovations. The study emphasizes the need to develop user friendly digital platforms, strengthen internet and technological infrastructure, and enhance government support through financial incentives and clear policy frameworks to encourage the broader use of agri-tech solutions. Overall, the research highlights the significant potential of digital agricultural technologies to improve farming practices, increase farmer earnings, and serve as a model that can be adapted to similar agricultural regions.

A Visual Analysis of the Development and Trends of Sustainable Communities: A Survey on Technology, User Needs, and Design

The 2023 Global Sustainable Development Report identified sustainable cities and communities as a critical area for in-depth review, emphasizing the need for systematically examining theoretical knowledge and guidance on the direction of development This article reviews the relevant literature from the Web of Science core database over the past decade and introduces Professor Verganti’s theoretical perspective of “design-driven innovation” to summarize technological research, user needs/demand, and design, providing a new theoretical dimension for the research on sustainable communities. To this end, this study employs three econometric tools—VOSviewer, RStudio Bibliometric, and CiteSpace—to analyze status and trends visually. The findings reveal that the design level has garnered the most research results, with the USA as the primary contributor and China as the country with the most development potential. Moreover, the most prominent research topics within the three perspectives are microbial communities, sustainable development goals, and ecosystem services, with recent research highlights focusing on artificial intelligence, social innovation, and tourism. In conclusion, this article proposes a strategic framework for the future development of sustainable communities, encompassing consolidation of technical foundations, clarification of demand orientation, and updating design specifications and theories to provide diverse solutions.

Industrialization meets sustainability: Analysing the role of technological innovations, energy efficiency and urbanisation for major industrialized economies

Considering how crucial environmental quality is to development, production often takes precedence over the development process when certain macroeconomic policies are being implemented. This phenomenon has been the subject of several studies conducted in various regions and nations. In this context, the recent article explores the nonlinear effects of industrial output, renewable energy, technological innovations, energy efficiency, and urbanization on CO2 emissions in the top ten industrialized countries. It recommends contradictory policy approaches due to its reported conflicting outcomes, opening up new research directions. To this end, the study relies on advanced econometric tools such as panel QARDL (Quantile Autoregressive Distributed Lag) and the nonparametric quantile Granger causality (NPQGC) test to attain robust results. The findings suggest that industrial output and urbanization significantly deteriorate environmental quality by increasing CO2 emissions across various time horizons. However, renewable energy, technological innovations, and energy efficiency have a significant influence towards enhancing environmental quality. Notably, industrialization and urbanization become environmentally friendly when energy efficiency is integrated with these variables. Additionally, the NPQGC test supports the main results by confirming the Granger causality between the modelled series. Based on the outcomes, the study suggests that the integration of energy efficiency with industrialization and urbanization can significantly contribute to achieving a sustainable environment.

Investigation of the Dynamic Relationship between Oil Temperature and Bearing Gearbox Condition Indicator Values for the Bell 407 Helicopter Based on Cointegration Analysis

The primary objective of this study is to investigate the dynamic relationship between oil temperature and the Bearing Gearbox Condition Indicator (BGCI) values of the Bell 407 helicopter. To achieve this goal, we employ robust econometric tools, such as unit root tests, cointegration tests, and Autoregressive Distributed Lag (ARDL) models for both, long-run and short-run estimates. Our findings indicate that variable temperature tends to converge to its long-run equilibrium path in response to changes in other variables. The results of the ARDL analysis confirm that spectral kurtosis, inner race, cage, and ball energy significantly contribute to the increase in temperature. Furthermore, we use the impulse response function (IRF) to trace the dynamic response paths of shocks to the system. The identification of a cointegrating relationship between oil temperature and BGCI values suggests a practical and significant connection that can potentially be used to predict hazardous changes in oil temperature using BGCI values, which is an important implication for enhancing the safety and reliability of helicopter operations. This study presents a promising direction for condition monitoring (CM) in rotating aircraft machinery, emphasizing the potential of integrating temperature data to simplify the diagnostic process while still achieving reliable results.

Carbon omission and financial market sustainability via government effectiveness: a cross-culture comparison of OECD and Asian emerging economies

PurposeFinancial market sustainability is gaining attention as investors and stakeholders become more aware of environmental, social and governance issues, pushing demand for responsible and ethical investment practices. Therefore, this study aims to investigate the impact of carbon (CO2) emissions from three sources, oil, gas and coal, on the stock market sustainability via effective government policies.Design/methodology/approachThe eight countries belong to two different regions of world: Asian economies such as Pakistan, India, Malaysia and China, and OECD economies such as Germany, France, the UK and the USA are selected as a sample of the study. The 22-year data from 2000 to 2022 are collected from the DataStream and the World Bank data portal for the specified countries. The generalized methods of movement (GMM) and wavelet are used as the econometric tool for the analysis.FindingsOur findings show that the CO2 emission from coal and gas significantly negatively impacts stock market sustainability, but CO2 emission from oil positively impacts stock market sustainability. Moreover, all the emerging Asian economies’ CO2 emissions from coal and gas have a much greater significant negative impact on the stock market sustainability than the OECD countries due to the critical situation. However, the government’s effective policies have a positive significant moderating impact between them, reducing the effect of CO2 emission on the stock market.Research limitations/implicationsThis study advocated strong implications for policymakers, governments and investors.Practical implicationsEffective government policies can protect the environment and make business operations suitable, leading to market financial stability. This study advocated strong implications for policymakers, governments and investors.Originality/valueThis study provides fresh evidence of the government’s effective role to control the carbon environment that provide the sustainability to the organizations with respect to OECD and emerging economy.

Informational Efficiency and Unbiasedness of Stock Futures in India—An Empirical Analysis

The present article examines the informational efficiency and unbiasedness of stock futures in India. The study analyses the daily spot and futures data of 20 companies listed on the National Stock Exchange (NSE) from 9 November 2001 to 30 July 2021. The study employs econometric tools like Johansen’s cointegration test, likelihood ratio test, vector error correction model and Chow breakpoint test. The results show that the futures prices of most companies are unbiased and efficient in the long run, as the null hypothesis of unbiasedness is not rejected. The error correction term is higher for the equity market than for the futures market of 13 companies. It indicates that when the futures prices and the underlying spot prices move apart from equilibrium in the short term, the spot market makes more adjustments to restore equilibrium. The structural breaks identified by the Chow breakpoint test did not affect the error correction process, as they were insignificant for most companies. The findings demonstrate that futures markets act as a central point for the dissemination of information to numerous buyers and sellers. Thus, market participants can rely on futures markets’ price movements for suitable trading strategies.

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.

Can artificial intelligence and green finance affect economic cycles?

The COVID-19 recession and the Ukraine-Russia War (URW) crisis have added a new layer of complexity to global economic cycles, necessitating the evolution of economic systems and proactive responses to emerging economic challenges. In this context, the recent article introduces artificial intelligence (AI) as a new driver of economic cycles and analyzes its dynamic role alongside the Belt and Road Initiative (BRI), the Paris Agreement (PA), green finance (GB), and economic shocks (ES) in determining global economic cycles. The article employs novel econometric tools, namely the CAViaR-TVP-VAR model, the Quantile Coherence method, panel Quantile on Quantile Kernel-Based Regularized Least Squares (PQQKRLS), and the Quantile-Quantile Granger causality (QQGC) test for robust findings. The outcomes reveal that AI influences economic cycles in the short run while significantly mitigating these cycles in the medium and long run. Furthermore, the BRI exhibits a positive link with economic cycles during the short and medium run; however, it can contribute to economic stability in the long run by impeding economic fluctuations. Similarly, green finance and the PA show mixed influences across various time horizons, except for the long run, which confirms their negative association with economic cycles. Additionally, ES has a direct link with economic cycles across most periods. The robustness check based on the QQGC test and PQQKRLS method supports the main results. Our results identify AI, BRI, and the PA as new drivers of economic cycles with the potential to counter global economic cycles. Therefore, based on these findings, the study proposes several policy implications tailored to different time horizons.

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.

Understanding temporal dynamics of jumps in cryptocurrency markets: evidence from tick-by-tick data

Cryptocurrency markets have recently attracted significant attention due to their potential for high returns; however, their underlying dynamics, especially those concerning price jumps, continue to be explored. Building on previous research, this study examines the presence and clustering of jumps in an extensive tick data set covering six major cryptocurrencies traded against Tether on seven leading exchanges worldwide over nearly 2.5 years. Our analysis reveals that jumps occur on up to 58% of trading days, with negative jumps predominating in both frequency and size. Notably, we observe systematic clustering of jumps over time, especially in Bitcoin and Ethereum, indicating interconnected market dynamics and potential predictive power for market movements. By employing high-frequency econometric tools, we identify temporal patterns in jump occurrence, highlighting heightened activity during specific trading hours and days. We also find evidence of jumps influencing intraday returns, underscoring their significance in short-term price dynamics. Our findings enhance understanding of the cryptocurrency market microstructure and offer insights for risk management and predictive modeling strategies. Nevertheless, further research is needed to develop robust methodologies for detecting and analyzing co-jumps across multiple assets.

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.