Relevant bibliographies by topics / Eight ordinary least squares (OLS) regression model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Eight ordinary least squares (OLS) regression model'

Author: Grafiati
Published: 19 February 2023
Last updated: 19 July 2025

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Journal articles on the topic "Eight ordinary least squares (OLS) regression model"

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Weya, Ince, Sirojuzilam Sirojuzilam, Muhammad Syafi’i, and Dede Ruslan. "Analysis of the Effect of HDI and Road Length Infrastructure Development on Improving Economic Inequality in Eight Districts of the Region La Pago Tradition." Journal of International Conference Proceedings 6, no. 5 (2023): 137–50. http://dx.doi.org/10.32535/jicp.v6i5.2655.

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The objective of this research is to examine the impact of human development index and road length infrastructure development on the reduction of economic inequality, as shown by general publication, throughout the period from 2013 to 2022. The inclusion of secondary data is essential in order to provide a comprehensive explanation or response to the research inquiry. The present work used a panel data model with a linear regression methodology, namely the ordinary least squares (OLS) method, for data analysis. The findings indicate that there is a substantial positive relationship between the
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Islam, Md Aminul, Tanzina Ahmed Rickty, Pramit Kumar Das, and Md Bashirul Haque. "Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data." Proceedings of Engineering and Technology Innovation 23 (January 1, 2023): 23–35. http://dx.doi.org/10.46604/peti.2023.9617.

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Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for modeling urban expansion. The model is calibrated for the years 2014 to 2017 using eight explanatory variables extracted from the regression model. The regression coefficients of the variables are found statistically significant at a 99% confidence level. The cellular automata (CA) model is then used to a
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Thein, Ei Ei, Atsushi Niigata, and Kazuo Inaba. "Information disclosure and SME financing: A study of firms in the ASEAN region." Journal of Accounting, Business and Finance Research 17, no. 2 (2023): 64–77. http://dx.doi.org/10.55217/102.v17i2.720.

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This study investigates the impact of information and communication technology (ICT) and audited financial statements on small and medium enterprise (SME) financing, as well as their influence on SMEs’ collateral issues in acquiring bank loans, based on the information asymmetry theory. The study applies the ordinary least squares (OLS) test, the two-stage least squares test, and the probit regression model for the analysis. The sample consists of 12,165 firms in eight ASEAN countries between 2009 and 2018. The data used in the analysis was sourced from the Business Environment and Enterprise
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Zhao, Peizhi, and Yuyan Wang. "How Does Economic Policy Uncertainty Affect Momentum Returns? Evidence from China." International Journal of Financial Studies 10, no. 3 (2022): 59. http://dx.doi.org/10.3390/ijfs10030059.

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Economic policy uncertainty has been identified as a new macroeconomic risk factor that harms the stock market’s profitability. This paper examines the impact of the Chinese EPU levels on one of the most famous financial anomalies—momentum returns. A new EPU index based on mainland China newspapers is used to obtain more accurate EPU–momentum relations. We selected 3958 Chinese listed companies’ stocks from 2011 to 2022 to establish time-series (TSM) and returns signal momentum strategies (RSM). Although the momentum effect in the Chinese stock market is weak, the EPU-based dynamic-threshold R
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Yang, Zhiheng, Chenxi Li, and Yongheng Fang. "Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China." Land 9, no. 1 (2020): 7. http://dx.doi.org/10.3390/land9010007.

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More and more studies on land transfer prices have been carried out over time. However, the influencing factors of the industrial land transfer price from the perspective of spatial attributes have rarely been explored. Selecting 25 towns as the basic research unit, based on industrial land transfer data, this paper analyzes the influencing factors of the price distribution of industrial land in Dingzhou City, a rural land system reform pilot in China, by using a geographically weighted regression (GWR) model. Eight evaluation factors were selected from five aspects: economy, population, topog
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Chen, Feng, Shenlong Lou, Qiancong Fan, et al. "Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation." Remote Sensing 11, no. 14 (2019): 1681. http://dx.doi.org/10.3390/rs11141681.

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Landsat 4-5, built at the same time and with the same design, carrying the Multispectral Scanner System (MSS) and the Thematic Mapper (TM) simultaneously, jointly provided observation service for about 30 years (1982–2013). Considering the importance of data continuity for time series analyses, investigations on the continuity of the Landsat 4-5 MSS and TM are required. In this paper, characterization differences between the Landsat 4-5 MSS and TM were initially discussed using the synthesized reflectance records generated from a collection of Hyperion hyperspectral profiles which were well ca
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Akande, Joseph Olorunfemi. "THE IMPACT OF ESG PRACTICES ON THE RISK PORTFOLIO OF LISTED OIL AND GAS FIRMS IN NIGERIA USING A MULTILAYERED CRITERION." Gusau Journal of Accounting and Finance 5, no. 2 (2025): 143–55. https://doi.org/10.57233/gujaf.v5i2.09.

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This study investigates the impact of Environmental, Social, and Governance (ESG) factors on the risk-adjusted returns of Nigerian oil and gas firms listed on the Nigerian Exchange Group (NGX) over a 11-year period (2012–2022). The study was anchored on signalling theory. Utilizing a correlational research design, data was collected from eight firms meeting inclusion criteria, focusing on ESG scores as independent variables, with Firm Size as a control variable, and risk-adjusted returns as the dependent variable. Diagnostic tests ensured adherence to Best Linear Unbiased Estimator (BLUE) assu
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Wali Ullah, G. M., Mohammad Nasrath Faisal, and Sadaqa Tuz Zuhra. "Factors Determining Profitability of the Insurance Industry of Bangladesh." International Finance and Banking 3, no. 2 (2016): 138. http://dx.doi.org/10.5296/ifb.v3i2.9954.

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Insurance is a form of risk management, used to hedge against the risk of a contingent loss. It involves the transfer of the risk of potential loss from one entity to another, in exchange for a risk premium. Insurance sector plays an important role in service based economy of both developed and developing markets. The purpose of this research is to analyze the determinants that serve as significant predictors of non-life insurance firms’ profitability in Bangladesh. It analyzes panel data of eight different insurance companies—selected using convenience sampling method from the years 2004-2014
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Basri, Mohd Faizal, Fitri Shuhaida Shoib, and Surianor Kamaralzaman. "Determinants of Capital Structure: Evidence From Malaysian Food and Beverage Firms." Research in World Economy 10, no. 5 (2019): 45. http://dx.doi.org/10.5430/rwe.v10n5p45.

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This paper investigates the firm-specific elements, which are profitability, growth, tangible assets and liquidity in determining the capital structure of Food and Beverage (F&B) firms in Malaysia. The research employed panel data regression model based on ordinary least square (OLS) method. The sample of research consists of eight firms listed in the food producer segment in Bursa Malaysia for the period between 2013 and 2018, with a total observation of 48 firms-years. Debt to equity was chosen as dependent variable. On the other hand, profitability, asset growth, tangibility of assets,
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CRUZ, Lucas Xavier Pereira, Sérgio Ricardo Miranda NAZARÉ, Danielle Montenegro Salamone NUNES, and Mariana Porto FERNANDES. "INCOME DIVERSIFICATION AND ITS EFFECTS ON PROFITABILITY AND RISK: A STUDY OF BRAZILIAN BANKS." Boletim de Conjuntura (BOCA) 21, no. 62 (2025): 133–53. https://doi.org/10.5281/zenodo.14908433.

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Banks generally have two main types of income: interest-related income, or simply interest income, and non-interest-related income, or non-interest income. This study presents an analysis of the impact that income diversification has on profitability (measured by ROE and ROA), insolvency risk (measured by ZScore), and risk-adjusted return (measured by the ratio of ROE and ROA to their respective standard deviations) of banks. The analysis considers diversification between income groups (interest and non-interest) and diversification within each group, where different types of interest and non-
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Dissertations / Theses on the topic "Eight ordinary least squares (OLS) regression model"

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Farshadfar, Shadi. "An Investigation of the Relative and Incremental Predictive Ability of Accrual- and Cash-Based Accounting Measures for Future Cash Flows: Australian Evidence." Thesis, Griffith University, 2009. http://hdl.handle.net/10072/365486.

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This research aims to present an empirical investigation of the relative and incremental predictive ability of alternative accrual- and cash-based accounting measures to forecast future cash flows in an Australian context. To achieve this goal, eight ordinary least squares (OLS) regression models on pooled time-series of cross-sectional data are developed. To evaluate the forecasting performance of the models, both within-sample and out-of-sample forecasting tests are employed. A number of contextual factors, including the length of the operating cash cycle, industry membership, firm profitabi
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Books on the topic "Eight ordinary least squares (OLS) regression model"

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Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.

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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R
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Book chapters on the topic "Eight ordinary least squares (OLS) regression model"

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COSTANTIELLO, Alberto, and Angelo LEOGRANDE. "The Impact of Government Expenditure on Education in the Environmental, Social and Governance Models at World Level." In Digital Future in Education: Paradoxes, Hopes and Realities. RITHA, 2023. http://dx.doi.org/10.57017/seritha.2023.dfe.ch9.

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In this chapter, we estimated the value of Government Expenditure on Education (GEE) in the context of Environmental, Social and Governance (ESG) dataset of the World Bank. We used data from 193 countries in the period 2011-2020. We used Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters. Finally, we confronted eight machine learning algorithms for the prediction of the future value of GEE. We found that the polynomial regression is the best predictive algorithm. The polynomial regression predicts an increase in GEE of 7.09% on average for the analysed countries.Keywords: collective decision-making; education; legislatures and voting behaviour; corruption; policy formulation.JEL Classification: D70; D72; D73; D78. Cite this chapter:Leogrande, A and Costantiello, A. (2023). The Impact of Government Expenditure on Education in the Environmental, Social and Governance Models at World Level. In L., Nicola-Gavrilă (Ed), Digital Future in Education: Paradoxes, Hopes and Realities, 217pp., ISBN: 978-606-95516-1-5. In Book Series Socio-Economics, Research, Innovation and Technologies (SERITHA), ISSN: 3008-4237, p.187-207. https://doi.org/10.57017/SERITHA.2023.DFE.ch9Chapter’s history: Received 11th of May, 2023; Revised 17th of June, 2023; Accepted for publication 20th of July, 2023; Published 30th of September, 2023. 
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Li, Quan. "Regression Diagnostics and Sensitivity Analysis." In Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.003.0006.

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This chapter shows why the Gauss-Markov assumptions are important in ordinary least squares (OLS) regression, how to diagnose assumption violations in OLS regression, and how to conduct sensitivity analysis and correct for some assumption violations. The issues covered include linearity and model specification, perfect and high multicollinearity, constant error variance, independence of error term observations, outlier and influential observations, and normality test. A mastery of materials in this chapter is necessary for systematic data analysis of a continuous outcome variable in a cross-sectional design
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Li, Quan. "Regression Analysis." In Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.003.0005.

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This chapter teaches how to use R to conduct regression analysis to answer the question: Does trade promote economic growth? It demonstrates how to specify a statistical model from a theoretical argument, prepare data, estimate and interpret the statistical model, and use the estimated results to make inferences and answer the question of interest. More specifically, it discusses the logic of regression analysis, the relationship between population and sample regression models, how to estimate a regression model in theory and practice, the estimation of sample regression model using OLS (ordinary least squares), the interpretation of estimation results, the statistical inference in regression analysis using hypothesis testing and confidence interval, the types of sum of squares and overall model fit, and how to report the model results. The validity of regression analysis is contingent upon the assumptions of the Gauss-Markov theorem being met.
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Rakers Remco. "The influence of sustainment contract types on product reliability." In Air Transport and Operations. IOS Press, 2012. https://doi.org/10.3233/978-1-60750-812-0-75.

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Aero engine Original Equipment Manufacturers (OEMs) generate more than half of their revenue from aftermarket services. This research investigates the effect of two main categories of sustainment contracts in this industry, namely Time & Material Contracts (T&MCs) and Outcome-Based Contracts (OBCs), on defense aero engine reliability. A Rolls-Royce dataset with military aero engine repairs is analyzed by using Ordinary Least Squares (OLS) regression and survival analysis with the semi-parametric Cox model and the parametric Weibull model. Results from an existing study on the civil sector are used to compare the defense with the civil aero engine aftermarket.
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Sümbül, Harun. "Deep Network Model and Regression Analysis using OLS Method for Predicting Lung Vital Capacity." In Business, Management and Economics. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104737.

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With the advancement of technology, many new devices and methods with machine learning and artificial intelligence (ML-AI) have been developed and these methods have begun to play an important role in human life. ML-AI technology is now widely used in many applications such as security, military, communications, bioengineering, medical treatment, food industry, and robotics. In this chapter, deep learning methods and medical usage techniques that have become popular in recent years will be discussed. Experimental and simulation results and a comprehensive example of the biomedical use of the deep network model will be presented. In addition, the regression analysis using the ordinary least squares (OLS) method for estimating lung vital capacity (VC) will be discussed. The simulation results showed that the VC parameter was predicted with higher than 90% accuracy using the proposed deep network model with real data.
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Nowak, Krzysztof, and Kamila Pietrzak. "Two Approaches to Analyzing Data of Project Teams." In Management Challenges in the Era of Globalization. University of Warsaw, 2019. http://dx.doi.org/10.7172/978-83-65402-94-3.2019.wwz.3.5.

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In this chapter, we compare and discuss several statistical approaches to analyzing data obtained from team members. As an illustrative example, we analyze data obtained from 636 team members across 87 teams from 8 companies located in Poland using ordinary least squares multiple regression analysis, multiple regression with clustered standard errors, and mixed modeling. In the example, we analyze the relationship between team size and team gender composition concerning team climate. While a model building approach to hypothesis testing yields most similar results for multiple regression an a clustered standard error approach, a multilevel model yields results more similar to OLS regression when testing the significance of individual predictors, suggesting a clustered standard error correction is more prone to a type II error rate when testing model coefficients than an equivalent multilevel model. Finally, the implications of these observations to team data analyses are discussed.
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Krishnan, Deepika, Mohsen Brahmi, and K. Archana. "Examining the Sector-Specific Ramifications of Initial Public Offering (IPO) Underpricing in the Indian Stock Market." In Advances in Finance, Accounting, and Economics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-5723-1.ch003.

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This study delves into the phenomenon of IPO (Initial Public Offerings) under-pricing in India and its implications on Nifty 50 index, both pre-pandemic and during the pandemic, with a specific focus on categorizing IPOs into three sectors: industrial, financial, and service. To analyse this, we employed the Market Adjusted Abnormal Return (MAAR) and Ordinary Least Squares Regression (OLS) model, which allowed us to investigate how market return, listing gain, oversubscription, issue price, and issue size impact IPO under-pricing and its subsequent effect on the Nifty 50 index. The results obtained through the OLS analysis revealed that, except for market index return, all the aforementioned factors exerted a significant influence on IPO under-pricing. Furthermore, we conducted t-tests to assess Market Adjusted Abnormal Return. This analysis revealed a significant degree of under-pricing in IPOs of industrial, financial, and service sectors.These findings serve as valuable insights for investors seeking to navigate the complexities of IPOs and their initial returns.
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Deulkar, Sundeep H., and Sagarika Pramod. "Enhancing STEM Education through Data Science: Predicting Electric Motor Performance with Linear and OLS Regression." In Computational Intelligence and Machine Learning. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-975670-5-6-9.

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This study bridges practical engineering tasks with data science analysis to enhance STEM education for high school students. In a hands-on exercise involving 60 teams of 12thgrade students, participants constructed electric motors to achieve maximum rotations per minute (RPM) using various values of magnetic field strength (B), current (I), number of coil turns (N), and coil cross-sectional area (A). We utilized this data to build and train linear regression and Ordinary Least Squares (OLS) regression models to predict RPM and power based on the measured values of B, I, N, and A. The analysis involved cleaning the data, performing regression, and evaluating the model’s performance using metrics such as R² values, p-values, Mean Squared Error (MSE), and model comparison criteria including AIC and BIC. The linear regression model for RPM yielded an R² value of 0.209 and a Mean Squared Error of 1043.14, indicating a weak fit. The OLS regression model for RPM also showed a low R² value of 0.205, with an adjusted R² of 0.046, and a non-significant F-statistic (p-value = 0.309). In contrast, the linear regression model for power resulted in an R² value of 0.450 and an MSE of 1090.11. The OLS regression model for power demonstrated a stronger fit with an R² value of 0.573, an adjusted R² of 0.487, and a significant F-statistic (p-value = 0.00136), along with lower AIC and BIC values compared to the RPM model. This approach not only facilitated practical learning but also demonstrated the application of data science in analysing and optimizing engineering designs. The results highlight the critical factors influencing electric motor performance and underscore the educational value of integrating data- driven analysis into STEM projects. This study provides valuable insights for future curriculum development, emphasizing the role of data science in enhancing experiential learning.
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Deulkar, Sundeep H., and Sagarika Pramod. "Enhancing STEM Education through Data Science: Predicting Electric Motor Performance with Linear and OLS Regression." In Advancements in Intelligent Systems. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-975670-3-2-9.

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This study bridges practical engineering tasks with data science analysis to enhance STEM education for high school students. In a hands-on exercise involving 60 teams of 12thgrade students, participants constructed electric motors to achieve maximum rotations per minute (RPM) using various values of magnetic field strength (B), current (I), number of coil turns (N), and coil cross-sectional area (A). We utilized this data to build and train linear regression and Ordinary Least Squares (OLS) regression models to predict RPM and power based on the measured values of B, I, N, and A. The analysis involved cleaning the data, performing regression, and evaluating the model’s performance using metrics such as R² values, p-values, Mean Squared Error (MSE), and model comparison criteria including AIC and BIC. The linear regression model for RPM yielded an R² value of 0.209 and a Mean Squared Error of 1043.14, indicating a weak fit. The OLS regression model for RPM also showed a low R² value of 0.205, with an adjusted R² of 0.046, and a non-significant F-statistic (p-value = 0.309). In contrast, the linear regression model for power resulted in an R² value of 0.450 and an MSE of 1090.11. The OLS regression model for power demonstrated a stronger fit with an R² value of 0.573, an adjusted R² of 0.487, and a significant F-statistic (p-value = 0.00136), along with lower AIC and BIC values compared to the RPM model. This approach not only facilitated practical learning but also demonstrated the application of data science in analysing and optimizing engineering designs. The results highlight the critical factors influencing electric motor performance and underscore the educational value of integrating data- driven analysis into STEM projects. This study provides valuable insights for future curriculum development, emphasizing the role of data science in enhancing experiential learning.
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Engle, Robert F. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation." In Arch. Oxford University PressOxford, 1995. http://dx.doi.org/10.1093/oso/9780198774310.003.0001.

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Abstract Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period fore cast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals.
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Conference papers on the topic "Eight ordinary least squares (OLS) regression model"

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Uderianová, Lucia, and Paulína Krnáčová. "The Impact of UNESCO Sites on International Tourism: The Relationship Between Cultural Heritage and International Tourism Arrivals." In 25th International Joint Conference Central and Eastern Europe in the Changing Business Environment. Vydavateľstvo EKONÓM, 2025. https://doi.org/10.53465/ceecbe.2025.9788022552257.363-375.

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Cultural heritage represents a key factor in destination attractiveness, with UNESCO World Heritage Sites playing a significant role in tourists decision-making processes. The aim of this paper was to explore the relationship between the number of UNESCO sites and the intensity of international tourism, measured by the number of tourist arrivals. A quantitative approach was adopted, using an Ordinary Least Squares (OLS) regression model, based on 2019 data from UNESCO and the World Bank. The analysis suggests that each additional UNESCO site is associated with an average increase of 2.03 milli
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Tang, Peng, Zhiguo Zhao, and Haodi Li. "Transient Temperature Field Prediction of PMSM Based on Electromagnetic-Heat-Flow Multi-Physics Coupling and Data-Driven Fusion Modeling." In SAE 2023 Vehicle Powertrain Diversification Technology Forum. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7031.

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<div class="section abstract"><div class="htmlview paragraph">With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable
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Anania, Jeniffer Charles, and Julian Lucas Kimario. "Influence of Technology-Related Factors on The Use of Mobile Money Services in Tanzania: The Moderating Role of Financial Literacy." In 16th International Operations Research Society of Eastern Africa Annual Conference. ORSEA Journal, 2025. https://doi.org/10.56279/orseaj.c2024.2.

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This study investigates the key factors influencing mobile money usage in Tanzania, focusing on technological determinants (mobile phone ownership, internet access, and bank account ownership), and demographic characteristics. Additionally, the study investigates the moderating role of financial literacy in shaping the relationship between these technological factors and mobile money usage. Grounded in the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB), the research uses a cross-sectional design with secondary data from the 2023 FSDT survey, including 9,915 individuals
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Birškytė, Liucija. "The impact of government debt on public finance stability in Lithuania." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.030.

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Purpose – though the global financial crisis is well behind us several EU countries continue to experience problems with public finance stability and need to cope with the consequences of high public debt. The purpose of the article is to find the relationship between government debt and of public finance stability in Lithuania. Research methodology – in order to achieve the aim of the article Financial Stability Index (FSI) for Lithuania has been created. It is based on theory and previous research. To find the determinants of FSI the multiple regression analysis model was specified and teste
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Blazheska, Angela, and Igor Ivanovski. "QUANTITATIVE ANALYSIS OF THE OPERATIONAL PERFORMANCE OF THE SELECTED NON-LIFE INSURANCE COMPANIES IN THE INSURANCE MARKET OF REPUBLIC OF NORTH MACEDONIA." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0030.

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The aim of this paper is to analyze the operational performance of the 5 dominant companies on the non-life insurance market in Republic of North Macedonia. As input in the analysis, the quarterly data for the 2009-2019 period is included for the key indicators such as the gross written premium (GWP), the gross liquidated damages, the number of insurance contracts and settled claims as well as the operating costs of the companies. These variables are observed through OLS (Ordinary Least Squares) regression analysis and VAR (Vector Autoregressive) model which demonstrates the dependence of the
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Malchev, Bojan. "Financial Performance Indicators and Stock Returns: A Decade-Long Analysis of MBI10 Firms in North Macedonia." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2023. http://dx.doi.org/10.47063/ebtsf.2023.0008.

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This paper investigates the relationship between financial performance indicators and annual stock returns of the MBI10 companies in North Macedonia over a ten-year period from 2013 to 2022. A total of 100 observations from the Macedonian stock market index (MBI10) are analyzed, using audited financial statements as the primary data source. The financial performance indicators studied include Return on Assets (ROA), Return on Equity (ROE), Earnings per Share (EPS), and Dividend per Share (DPS). A multiple linear regression model is applied to examine the impact of these indicators on annual st
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Silva, Ana Paula Trocoli da. "The impact of responsibility in fiscal management on the financial condition of municipalities." In V Seven International Multidisciplinary Congress. Seven Congress, 2024. http://dx.doi.org/10.56238/sevenvmulti2024-088.

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Responsibility in fiscal management, in accordance with the Fiscal Responsibility Law, presupposes planned and transparent action, through compliance with limits and conditions, including in relation to registration in Remains Payable. The present study sought to identify the influence of responsibility in fiscal management on the financial condition of municipalities. To this end, the influence of the main variable Remaining Payable on the financial situation of municipalities was analyzed, using the econometric model based on Martins et al. (2021). The financial condition is represented by t
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