Variance Inflation Research Articles

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.

Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR.

Turning Lemons into Lemonade: Mediating Collaborative Innovation through Digital Transformation for Sustainable Entrepreneurship

This study employs structural equation modelling and a 5-point Likert scale to examine the relationship among Digital Transformation, Collaborative Innovation, and Sustainable Entrepreneurship. Utilising Variance Inflation Factors (VIF) for collinearity assessment, the inner model demonstrates stable construct relationships despite moderate VIF values. When collinearity is evaluated using Variance Inflation Factors (VIF), the inner model shows robust construct correlations even with moderate VIF values. The results of path coefficient analysis show that collaborative innovation and digital transformation have a major, beneficial impact on sustainable entrepreneurship. The psychometric assessments ensure the reliability and validity of the constructs in the measurement model. The model selection of the study based upon Bayesian Information Criterion (BIC), sustains the efficacy of models, incorporating Collaborative Innovation and Sustainable Development by accentuating their critical charities to accomplish a frugal and effective model fit, vibrant for understanding multifarious phenomena. Specifically, f-square values highlight the practical significance of effect size measurements. Overall, this study offers a clear knowledge of the connections between these important concepts, which will be helpful for practitioners and policymakers as they promote sustainable entrepreneurship through collaborative innovation and digital transformation.

Examining the Impact of Oil Revenue on Inflation Rates in Iraq from 1991 to 2020

The Iraqi economy is primarily dependent on oil exports, which has a substantial impact on the inflation rate and economic stability. Despite substantial studies on the relationship between changes in oil prices and economic activity in both rich and developing countries, studies that specifically investigate the effects of oil prices on different economic sectors, notably in Iraq, remain few. The impact of oil revenues on the inflation rates in the Iraqi economy from 1991 to 2020 is investigated using regression analysis of historical data. To capture both long-term and short-term asymmetric interactions between variables, a nonlinear ARDL method is used. Increases in oil revenues, according to the study, will result in higher inflation rates, while losses in oil revenue will result in lower inflation rates. Understanding the factual connection that exists between the two variables (inflation rates and oil prices) in Iraq is critical, especially for monetary authorities in charge of inflation management. This insight enables them to develop appropriate policies to reduce the inflationary effects of rising oil prices. The study’s conclusions will be useful to policymakers, economists, and stakeholders interested in Iraq’s economic progress and stability.

The Effect of Macroeconomic Variables on the Profitability of Islamic Commercial Banks in Indonesia

Islamic banks as a form of banking that operates based on sharia principles, are expected to show superior financial performance when compared to interest-based conventional banks. This study aims to determine the effect of inflation, JUB, and PUAS on the profitability of Indonesian Islamic Commercial Banks. The method used in this research is a deductive quantitative method, with the Vector Autoregressive (VAR) Vector Error Correction Model (VECM) approach and the E-Views 12 test tool. The data used in this study used secondary data with time series data from the period January 2013 to April 2023, where ROA was used as an independent variable and Inflation, Money Supply, and Islamic Interbank Money Market became the dependent variable. As for the results of this study, in the long term, the variables of Inflation and JUB have a significant and negative effect on the Return on Assets (ROA) of Indonesian Islamic Commercial Banks, while the Islamic Interbank Money Market (PUAS) variable does not have a significant and negative effect on the Return on Assets (ROA) of Indonesian Islamic Commercial Banks. In the short term, the variables of Inflation, Money Supply (JUB), and the Islamic Interbank Money Market (PUAS) do not have a significant and negative influence on the Return on Assets (ROA) of Indonesian Islamic Commercial Banks.

The Influence Factors on Stock Returns of Real Estate and Property Companies Listed in Bei in 2010 - 2014

This research seeks to examine and offer insights into the factors that impact stock returns of real estate and property companies listed on the Indonesia Stock Exchange. The examined factors include DER, EPS, NPM, PBV, inflation, exchange rate, SBI, and GDP. The research focuses on real estate and property companies listed on the Indonesia Stock Exchange during the 2010-2014 period, involving a total of 38 companies. The study analyzes the impact of these variables DER, EPS, NPM, PBV, inflation, exchange rate, SBI, and GDP on the stock returns of these companies. Data were collected from sources such as Bank Indonesia, the Central Bureau of Statistics, and the Indonesia Stock Exchange. The findings reveal that DER, EPS, NPM, and PBV significantly influence stock returns, whereas inflation, exchange rate, SBI, and GDP do not have a significant impact on the stock returns of real estate and property companies.

Analysis on Influencing Factors of Grain Yield under the Grain Supply Chain in Heilongjiang

Limited agricultural production combined with the increasing food demand has led to the Grain Supply Chain instability and food supply conflicts, particularly in Heilongjiang province, a major grain-producing region of China. Increasing grain yield has become the key area of study among researchers. This study constructs a Multiple Linear Regression model based on Stepwise Regression analysis with R software and Eviews to investigate eight factors influencing grain yield in Heilongjiang province from 1992 to 2022 by utilizing methods such as White-test, Durbin-Watson Test, and Variance Inflation Factor to validate this model. According to the findings, an increase in Grain Sown Area, Effective Irrigated Area, and Value Added in Primary Industry, correlates with an increase in Grain Yield. In contrast, an increase in Disaster-Affected Areas and Employment in Primary Industry is associated with a decrease in Grain Yield. In addition, an in-depth analysis regarding the negative coefficient associated with Employment in the Primary Industry was conducted by integrating the Law of Diminishing Marginal Returns. It was concluded that the incompatibility of the initial hypothesis may be attributed to the educational level of Employment in Primary Industry and the efficiency of grain production. To establish a more stable, efficient, and flexible Grain Supply Chain system, it is recommended to enhance resource utilization, strengthen disaster monitoring, control inefficient labor productivity, and promote industrial collaboration to satisfy the increasing demand for boosted Grain Yield.

Factors associated with care-resistant behavior in the National Alzheimer's Coordinating Center's Uniform Data Set.

Little is known about the factors associated with care-resistant behavior in community-dwelling persons living with dementia. Regression modeling was performed on 41,143 responses to a standardized questionnaire from the National Alzheimer's Coordinating Center's Uniform Data Set. In the fully adjusted mixed-effects regression model, collinearity was low, with no variance inflation factor above 1.15. Moderate (adjusted odds ratio [aOR] = 1.59; 95% confidence interval [CI]: 1.50, 1.69) and severe (aOR = 1.95; 95% CI: 1.82, 2.09) dementia severity; and Black (aOR = 1.64; 95% CI: 1.47, 1.82), Hispanic ethnicity (aOR 1.13; 95% CI: 1.00, 1.28), frontotemporal (aOR 1.29; 95% CI: 1.12, 1.49) and Lewy body dementia (aOR 1.15; 95% CI: 1.02, 1.29) diagnosis type were associated with a higher odds of care-resistant behavior. Female sex (aOR 0.85; 95% CI: 0.80, 0.91) and higher education (aOR 0.9, 95% CI: 0.84, 0.97) were associated with lower odds of care-resistant behavior. Interventions to manage care-resistant behaviors should consider sociocultural factors as well as type of dementia diagnosis to limit caregiver strain and burden. Frontotemporal dementia, Lewy body dementia, and Black race were associated with a higher likelihood of exhibiting care-resistant behavior. Female sex and higher educational attainment were associated with a lower likelihood of exhibiting care-resistant behavior. Differences in specific presentations of care-resistant behaviors are less understood.

Social Determinants of Sepsis Mortality in the United States: A Retrospective, Epidemiologic Analysis.

To determine whether neighborhood-level social determinants of health (SDoH) influence mortality following sepsis in the United States. Retrospective analysis of data from 4.4 million hospitalized patients diagnosed with sepsis, identified using International Classification of Diseases-10 codes, across the United States. De-identified, aggregated data were sourced from the TriNetX Diamond Network. SDoH variables included income, housing cost burden, broadband access, park proximity, racial/ethnic diversity, and the Area Deprivation Index (ADI). The primary outcome was mortality, assessed using univariate and multivariate binomial generalized linear models. Predictors with high multicollinearity (Variance Inflation Factor > 5) were excluded to enhance model stability. Lower median income, higher ADI scores, limited park access, and lack of broadband connectivity were strongly associated with increased sepsis mortality. Unexpectedly, greater racial/ethnic diversity was negatively associated with mortality, possibly reflecting regional disparities in healthcare access and socioeconomic conditions. Multivariate analyses revealed that the inclusion of SDoH variables attenuated some effects observed in univariate models, highlighting their complex interplay. Random Forest analysis identified park access as the most important predictor of sepsis mortality, emphasizing its role as a potential proxy for broader neighborhood resources. Neighborhood-level SDoH are critical for risk stratification in sepsis prognostic models and should be systematically integrated into predictive frameworks. These findings highlight the need for targeted public health interventions to address social vulnerabilities, enhance access to green spaces, and reduce disparities in sepsis outcomes across diverse populations.

Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning

Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, using data collected between January 2020 and December 2022. By integrating advanced analytics, we identified key drivers of methane concentrations, including herd genetics, feeding practices, and management strategies. Statistical tools such as Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) addressed multicollinearity, stabilizing predictive models. Machine learning approaches—Random Forest and Neural Networks—revealed a strong negative correlation between methane concentrations and the Estimated Breeding Value (EBV) for protein percentage, demonstrating the potential of genetic selection for emissions mitigation. Our approach refined concentration estimates by integrating satellite data with localized atmospheric modeling, enhancing accuracy and spatial resolution. These findings highlight the transformative potential of combining satellite observations, machine learning, and farm-level characteristics to advance sustainable dairy farming. This research underscores the importance of targeted breeding programs and management strategies to optimize environmental and economic outcomes. Future work should expand datasets and apply inversion modeling for finer-scale emission quantification, advancing scalable solutions that balance productivity with ecological sustainability.

Using Minimum Travel Time to Determine Factors Influencing Travel Time Discrepancy and Variability in Tram Transit

Quality of service in urban surface transit has a great impact on sustainable urban mobility, with travel time being one of the most important indicators of service reliability. But urban surface transit is prone to many disturbance factors causing travel time discrepancy and variability, making transit less reliable for passengers. We conducted research in the City of Zagreb on a single tram line by splitting it into constant-frequency segments. The first phase was modeling minimum segment travel times to base the indicators and predictors upon, and the second phase was establishing correlation matrices between the predictors and travel time using Pearson correlation coefficients and significance. Variance inflation factors were used to check for collinearities. While predictors belonging to the transport supply irregularity group did not have an impact, the ones belonging to the disturbance factors group showed correlation, with six of them being significant. This research in rarely represented tram transit determined the most significant disturbance factors rich in traffic context that can be used to develop travel time prediction models.

PENGARUH KEBIJAKAN VAKSINASI COVID-19 DAN INFORMASI KEBIJAKAN TAPERING OFF TERHADAP IHSG TAHUN 2021-2022

Currently, there is a Covid-19 pandemic that is spreading throughout the world, including Indonesia. Then the government carried out a Covid-19 Vaccination policy to overcome this problem. Along with the implementation of the Covid-19 Vaccination policy, there was information tapering off information policy. What has an impact on the financial system and the real sector is the depreciation of the exchange rate and the high value of inflation. This resulted in capital outflows which had an impact on the ICI movement. The purpose of this study is to determine the impact of covid-19 vaccination, exchange rates, and inflation on ICI in 2021-2022. Research model used VECM (Vector Error Corection Model) with the help of research tools E-Views. The results obtained from this study show that the Covid-19 Vaccination Policy has an influence on the ICI and based on the IRF analysis of trends due to shock in the peiode has a positive influence. Information on tapering off policy through exchange rate and inflation variables has an influence on the ICI and based on IRF analysis the trend due to shock in that period has a negative influence.

The Impact of R&D and Innovation on National Income

In this study we empirically investigate the influence of several indicators of R&D and innovation on GDP for OECD countries by utilizing an unbalanced panel for the years of 1995-2021. Two distinct measures of patent applications, two different measures of trademark applications, researchers in R&D, and technicians in R&D are the proxies for R&D and innovation. We also added inflation, productivity, investment, and education into our model as control variables. Selection between fixed effect model and random effect model is made via Hausman test. According to the estimation results, we identified statistically significant positive impact of two distinct measures of patent applications, two different measures of trademark applications, researchers in R&D, and technicians in R&D variables on gross domestic product for the sample of OECD countries during the period of 1995-2021. Regarding the control variables, whenever they are statistically significant then we observed positive coefficient estimations for the variables of productivity, investment, and education and negative coefficient estimation for the variable of inflation.

Russia-Ukraine Geopolitical Crisis Indirect Effects on Indonesia's Financial System Stability

The Russia-Ukraine geopolitical crisis that has occurred since February 2022 is known to have resulted in a surge in food and energy prices at the global level. The escalation of the situation has expanded to cause the Fed to respond by raising the benchmark interest rate. The interest rate hike is caused by high inflation, which reduces the purchasing power of the domestic community. Financial system stability in Indonesia is essential, considering that US monetary policy is still the mecca of the country's economy. In addition, the dollar currency is still the primary means of payment in international trade. This study was conducted to analyze whether this geopolitical crisis indirectly influences Indonesia's financial system stability. The research method used to examine is the Error Correction Model (ECM). The study results show that the interest rates of the US, JCI, Money Supply, and government bonds significantly affect the short and long term. Meanwhile, inflation variables only have a significant effect in the long term. This research has policy implications where synergy resilience is needed to maintain proactive and holistic risk management in systemic. It also evaluates potential dangers and develops methods to reduce NPL to maintain stability and sustainability during changes in change and interactions between institutions included in the Financial System Stability Committee.

PENGARUH UANG ELEKTRONIK, SUKU BUNGA, DAN INFLASI TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA TAHUN 2015.I – 2023.IV

This study aims to test and analyze the effect of electronic money, interest rates, and inflation on economic growth in Indonesia in 2015.I-2023.IV. The type of research used is explanatory research. The research data source is secondary time series data in quarterly form, namely the first quarter of 2015 to the fourth quarter of 2023. The analysis model used is multiple linear regression analysis. Based on the results of partial hypothesis testing, it is concluded that the electronic money variable, and interest rates have no significant effect, while the inflation variable has a significant effect on economic growth. Simultaneously, the variables of electronic money, interest rates, and inflation simultaneously have a significant effect on economic growth. The acceleration of digital transactions should be accompanied by the availability of adequate infrastructure and simplify business regulations and increase market confidence so as to encourage economic growth.

Investigating the Relationship between Corporate Social Responsibility and Financial Performance: A Case Study on Indian Manufacturing Companies

This study investigates the relationship between corporate social responsibility (CSR) and corporate financial performance (CFP) in the context of Indian manufacturing firms. Using secondary data from 150 firms listed in the Nifty 500 index over six years (2014–2019), the study employs multivariate regression analysis to examine both accounting-based (ROA) and market-based (Tobin's Q) performance measures. The results reveal a significant positive association between CSR activities and CFP, highlighting that CSR initiatives enhance profitability and market valuation. Variance inflation factor and stationarity tests confirm the robustness of the models, with R-squared values indicating that independent variables explain 66–67% of the variation in financial performance. The findings underscore the strategic importance of CSR in improving economic outcomes and guiding managerial decisions. While limited to Indian manufacturing firms, the study provides a foundation for further research in cross-country and sectoral comparisons.

ANALISIS PENGARUH VARIABEL MONETER INDONESIA DAN INDEKS SAHAM NEGARA LAIN TERHADAP IHSG

This study aims to determine the effect of Indonesian monetary variables and other countries' stock indices on the Composite Stock Price Index (JCI). The data used is monthly time series data from January 2020 to December 2023. The method used in this study is the Vector Error Correction Model (VECM) method which is determined from the results of the stationarity test, lag determination, cointegration test. The independent variables used include monetary variables (inflation and exchange rates) and stock indices of other countries (nikkei 225 and Dow Jones Industrial Average /DJIA). The dependent variable used is the JCI. The results showed that all monetary variables (inflation and exchange rates) had no influence in the short term, while in the long term both monetary variables had a negative effect on the JCI. Meanwhile, other countries' stock index variables, namely the DJIA, negatively affect the JCI both in the short and long term. Nikkei 225 has a positive short term effect on JCI, while in the long term nikkei 225 has no effect on JCI.

PENGARUH PENANAMAN MODAL ASING (PMA), PENANAMAN MODAL DALAM NEGERI (PMDN) DAN INFLASI TERHADAP PENERIMAAN PAJAK DI INDONESIA TAHUN 2014-2023

taxes are an important component of a country's wealth and monetary level, enabling the country to produce output in the form of state revenue. The study aims to determine the effect between foreign investment, domestic investment and inflation on tax revenue in Indonesia. This study was conducted with the aim of knowing what factors can affect tax revenue in Indonesia, which is also the main source of tax revenue in Indonesia. This study uses quantitative methods with an associative approach while the analysis method used is multiple linear regression analysis. The results obtained from this study are that tax revenue in Indonesia is negatively affected by the variables of foreign investment, domestic investment and inflation.

Retrospective Study on the Association Between Climate Factors and Infant Colic in Beijing (2021-2022).

To assess seasonal variations in infant colic (IC) prevalence and explore the association between climate factors, including temperature, air pollutants, and their interactions, with IC. Medical records of 1955 infants aged 0-3 months from October 2021 to September 2022 were analyzed, with IC diagnosed according to Rome IV criteria. Seasonal differences in IC prevalence were compared using chi-square tests. Climate data, including weekly averages of temperature, PM2.5, PM10, NO2, CO, O3, and AQI, along with 1-, 2-, and 4-week lags, were collected via web scraping. Interaction terms between temperature and pollutants (including lagged variables) were created. Variance Inflation Factors (VIF) addressed multicollinearity. Pearson correlation assessed linear relationships, while Generalized Additive Models (GAM) evaluated non-linear associations. The overall IC prevalence was 38.62%. Demographic analysis showed no significant differences between infants with and without IC. Seasonal analysis revealed significant differences, with the highest IC prevalence in winter. After Bonferroni correction, spring (34.52%) and winter (43.60%) differed significantly (p < 0.0083). Linear correlation analysis indicated weak associations between temperature, pollutants, and their interactions with IC (correlation coefficients: -0.05 to 0.03). GAM confirmed these findings, with individual climate factors explaining only 0.002 of the deviance and their interactions explaining 0.007. No meaningful relationship between climate factors and IC prevalence was identified. This study identified significant seasonal differences in IC prevalence, with the highest rates observed in winter. However, no significant linear or non-linear associations were found between IC and temperature, air pollutants, or their interactions. These findings underscore the need for future research to explore non-climatic factors.

Determinats factors self-management barriers: characteristics, spiritual well-being, and religiosity in type 2 diabetes patients

Holistic nursing needs to evaluate barriers to self-management, insight from characteristics, religiosity, and spiritual well-being to understand and address the multifaceted needs of individuals with chronic conditions, such as diabetes mellitus type 2. This study aimed to identify the determining factors that affect the barriers to self-management and the variables affecting them among Indonesian patients with T2DM. A cross-sectional survey was conducted among 101 patients recruited via purposive sampling from March to May 2023. The research instruments used were the Spiritual Well-Being Scale, Islamic Religiosity Scale, and Summary of Diabetes Self-Care Activities. The potential multicollinearity effects among the predictors of the barriers to self-management were also evaluated using multiple linear stepwise regression and collinearity analysis of variable inflation factors (VIFs). Most of the respondents had moderate spiritual well-being (52.2%) and high Islamic religiosity (60.4%) but also had barriers to self-management (55%). The F value of 5.888 with a probability of 0.004 (&amp;lt;0.05) showed that based on their regression coefficients, spiritual well-being, and Islamic religiosity simultaneously affected the barriers to self-management by 10.7%. The determinants that were found to affect the barriers to self-management were spiritual well-being and religiosity. These results suggest that it is necessary to develop an intervention model to improve the self-management of T2DM with a holistic approach.