Classical Regression Estimation Versus Bayesian Regression Estimation: A Simulation-based Analysis of Predictive Performance

Purpose This study aims to contribute to the ongoing assessment of executive compensation by investigating the nexus between managerial entrenchment factors, adopting a multifaceted perspective encompassing both economic and non-economic dimensions. Design/methodology/approach This research employs pooled cross-sectional Ordinary Least Squares (OLS) regression and Least Squares with Dummy Variables (LSDV) models with fixed effects to examine the determinants of Chief Executive Officer (CEO) compensation. Findings This research identifies firm size, performance (via ROA and Tobin’s Q), and CEO characteristics (age, tenure, stock ownership, MBA degree) as significant determinants of executive compensation at the 0.05 level. In contrast, the prestige of educational institutions, doctoral degrees, and the MBA’s relevance to short-term performance, along with CEO tenure, do not significantly affect pay. Additionally, the study highlights the significance of industry type (manufacturing vs technology) in shaping compensation, emphasizing the role of firm metrics and CEO credentials in designing executive pay packages. Originality/value This research introduces an innovative approach to controlling unobserved heterogeneity and adjusting for the dynamic nature of CEO compensation attributes across diverse CEO characteristics. By integrating both pooled Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) models, the study addresses the challenges posed by time-invariant variables and unobservable heterogeneity. Such issues have historically skewed the accuracy of traditional OLS models in identifying the comprehensive array of factors—both economic and non-economic—that influence CEO compensation. This novel methodological framework significantly advances the examination of unobservable variables that may vary not only across the firms selected for analysis but also over time periods, thereby offering a more detailed understanding of the determinants of CEO pay.

This research article primarily focuses on the estimation of parameters of a linear regression model by the method of ordinary least squares and depicts Gauss-Mark off theorem for linear estimation which is useful to find the BLUE of a linear parametric function of the classical linear regression model. A proof of generalized Gauss-Mark off theorem for linear estimation has been presented in this memoir. Ordinary Least Squares (OLS) regression is one of the major techniques applied to analyse data and forms the basics of many other techniques, e.g. ANOVA and generalized linear models [1]. The use of this method can be extended with the use of dummy variable coding to include grouped explanatory variables [2] and data transformation models [3]. OLS regression is particularly powerful as it relatively easy to check the model assumption such as linearity, constant, variance and the effect of outliers using simple graphical methods [4]. J.T. Kilmer et.al [5] applied OLS method to evolutionary and studies of algometry.

Objective - The poverty issue in East Java Province is an interesting research object. This phenomenon has retrieved in every fiscal year, although the intergovernmental transfer funds increase significantly annually. In the decentralization era, a region has been authorized to identify its problem and provide solutions based on their initiatives and preferences. The local government through their budget should focus on their problem solving, i.e. poverty alleviation. Utilizing panel regression, we found that government spending on education and health can reduce poverty rate. Methodology/Technique - OLS (Ordinary Least Squared) modelwas utilized to answer the objective of the study, that is, to see how the effect of government spending in various sectorson poverty alleviation. Findings - Using the panel regression model, this study found that government spending on education has a negative impact on poverty rate in the East Java Province. The impact on this variable appears to be statistically significant. Novelty - This study showed that central and local government should synchronize their proposed programs, reducing overlapped programs, to pursue a higher efficiency of budget management. Type of Paper - Empirical/Review Keywords: Quality Spending, Budget Deficit/Surplus, Poverty Alleviation. JEL Classification: H72, I31, I32.

This study investigates the interconnections among sustainability reporting, social performance, and firm value across the BRICS nations (Brazil, Russia, India, China, and South Africa). Employing a quantitative research design, the study utilizes firm-level data from the Refinitiv database, covering 862 firms operating in the BRICS countries from 2017 to 2022. The analysis begins with Ordinary Least Squares (OLS) regression and extends to models incorporating year-fixed effects and firm-fixed effects to account for heterogeneity and omitted variable bias. Robustness checks are conducted using OLS regression with robust standard errors, fixed effects regression with Driscoll–Kraay standard errors, and an instrumental variable approach to address potential endogeneity concerns. To examine the moderating role of sustainability reporting, interaction terms are incorporated into the regression models and margin plots are used for visualization. The findings reveal that social performance positively impacts firm value, underscoring the role of social responsibility in driving financial performance. Furthermore, sustainability reporting strengthens this relationship, indicating that firms with well-established reporting frameworks can effectively leverage social initiatives to enhance market valuation. Therefore, this study contributes to the literature by providing empirical evidence on the moderating effect of sustainability reporting in emerging markets. The findings offer valuable implications for policymakers, investors, and corporate leaders seeking to optimize CSR strategies and enhance firm value in dynamic economic environments.

This research examines the factors that influence health-related stereotypes against immigrants, with a focus on how human capital, conservative ideology, and economic anxiety impact these perceptions across different immigrant groups in the United States. The study uses Ordinary Least Squares (OLS) regression analysis of data from the 2020 Collaborative Multi-racial Post-Election Survey to assess attitudes towards all immigrants, as well as specifically Mexican and Chinese immigrants. Objective: Health stereotypes often depict immigrants as carriers of diseases and burdens on society, which affect public opinion and policy. This study aims to analyze the roles of human capital, political ideology, and economic conditions in shaping these stereotypes. Method: Using OLS regression, the analysis explores the association between stereotyping and various predictors, including conservative ideology (measured objectively and subjectively), human capital, and economic anxiety. Disaggregating the data by immigrant group allows for nuanced insights into the interplay of these factors. Results: The results reveal moderate levels of stereotyping across all groups, with variations by immigrant origin. Objective measures of conservative ideology show a strong correlation with increased stereotyping, particularly against Mexican immigrants. Higher human capital is associated with reduced stereotyping, while economic anxiety consistently exacerbates negative stereotypes across all groups. Furthermore, Republican affiliation and racial identity are correlated with heightened stereotyping, especially against Chinese immigrants. Discussion: The findings highlight the complexity of the factors contributing to health-related stereotypes and emphasize the importance of addressing educational and economic elements to mitigate these biases. Tailored interventions that take into account the distinct dynamics of each immigrant group are crucial for reducing stereotypes and improving integration and health outcomes.

The relationship between stock trading volume and stock price has been widely debated, with prior research reporting both positive and negative correlations. Understanding this relationship is essential for identifying potential arbitrage opportunities and anticipating market movements. This study examines the correlation between trading volume and price by applying an Ordinary Least Squares (OLS) regression model specifically to index fund data. The analysis reveals a modest correlation of 3.25%, with an adjusted value of 3.22%, suggesting that while the relationship is weak, the variables are not statistically independent. Additionally, the results indicate that, for the given dataset, the Laplacian error metric offers a better fit than the Gaussian metric. These findings provide a nuanced perspective on volume–price dynamics and highlight the potential benefits of alternative error modeling approaches in financial time series analysis.

The effective shear strength is a critical parameter for evaluating ultimate and serviceability limit states of geotechnical structures. To conduct a fully probabilistic assessment or to determine characteristic values according to the second generation of Eurocodes, it is essential to quantify the uncertainty of ground properties due to inherent variability, measurement error, transformation, and statistical uncertainty. However, unlike other ground properties, shear strength parameters are not directly measured, even in laboratory settings. Instead, they are derived from the relationship between shear and normal stresses, making uncertainty analysis nontrivial. This study applies two regression approaches and the extended multivariate approach (EMA) to estimate the effective friction angle for non-cohesive soils. Firstly, an ordinary least squares (OLS) and a Bayesian linear regression (BLR) approach are utilized to quantify the uncertainties inherent in data from direct shear and triaxial tests from an offshore wind project. Secondly, the EMA is utilized to integrate cone penetration tests (CPT) and shear test data via Bayesian inference. The results are discussed based on characteristic values according to Eurocode 7 (EN 1997-1:2024) highlighting the importance of accurately and precisely estimating mean and uncertainty.

Corn is the second most important component of the grain segment after soybeans, averaging close to 35.7% of total grain traffic through the Panama Canal. The objective of this paper is to attempt to fit a preliminary general demand model for corn traffic through the Panama Canal using Ordinary Least Square (OLS). The corn traffic estimated is the U.S. Gulf and East Coast to East Asia, particularly China, Japan, South Korea, and Taiwan, and the research hypothesized the possible variables that may explain the downward trend inthe movements of corn in this route between October 2004 to September 2022. Canal costs, U.S. Gulf freight rates, U.S. Gulf and Pacific Northwest grains inspections and the energy index were the most important explanatory variables in the study. This research also discusses the future of corn traffic through the waterway in terms of alternative sources, routes, and possible demand for corn, and explores the decarbonization process impacting the Panama Canal and the U.S. corn supply chain. For the literature review, the research is leveraging on previous estimation of demand functions for grains and the decarbonization studies related to the maritime industry, and examine papers related to Panama Canal shipping demand, thus closing the gap on the literature about transportation demand through the waterway.

Indonesia is one of the countries that are active in international trade activities. This study examines the effect of macroeconomic variables consisting of natural resources rent, foreign direct investment, and government spending on Indonesian exports. In addition to macroeconomic variables, this study examines the effect of the Russia-Ukraine war on Indonesian exports. This study uses time series data from 1991-2021. The method used in this research is Ordinary Least Square (OLS). The results of this study show that natural resource rents have a positive effect on exports. In contrast, foreign direct investment, government spending, and the Russia-Ukraine war negatively and significantly impact Indonesian exports from 1991-2021. Simultaneously, natural resource rents, foreign direct investment, government spending, and the Russia-Ukraine war substantially affect Indonesian exports. This paper examines how macroeconomic factors, particularly the influence of the Russia-Ukraine war on exports, affect Indonesian exports, which are currently quite restricted. As a result, this study helps to understand the variables that influence exports in Indonesia.

This study examined the relationships among oil revenue, government spending, and economic growth in Nigeria. By implication, it investigated whether oil revenue impacted on government spending, as well as on economic growth in the country over the period from 1980 to 2012. Time series data were analyzed using econometric techniques which included Ordinary Least Square (OLS), cointegration, Vector Error Correction Model (VECM), and Granger causality to determine the direction of causality and the magnitude of impacts of the variables. Findings from the analysis revealed that oil revenue Granger caused both of total government spending and growth, while there was no-causality between government spending and growth in the country. The study therefore suggested that government should increase spending on capital projects as well as intensify efforts at increasing output in the oil sub-sector in order to boost economic growth in Nigeria.

This research analyzes the influence of leading economic sectors and government spending on economic growth in the Limapuluh Kota Regency. Secondary data is used to obtain information needed for research analysis. The analytical methods used are quantitative descriptive analysis and qualitative analysis. The qualitative analysis uses the Location Quotient (LQ) method and Growth Ratio Model (GRM) to identify leading sectors in the Limapuluh Kota Regency. The Ordinary Least Square (OLS) approach analyzes the influence of leading economic sectors and government spending on economic growth statistically. The results of this research found that 3 sectors were categorized as superior using the LQ method and 8 sectors were categorized as potential using the GRM method. Then a regression analysis was carried out with the results that the mining sector and government spending did not significantly affect economic growth. Meanwhile, the agricultural sector and the construction sector have a significant influence on economic growth in Limapuluh Kota Regency.

Background: A review of the health behavior literature on the statistical modeling of days of physical activity (PA) indicates that in many instances linear regression models have been used. It is inappropriate statistically to model a count dependent variable such as days of physical activity with Ordinary Least Squares (OLS). Many count variables have skewed distributions, and, also, have a preponderance of zeroes. Count variables should not be treated as continuous and unbounded. If OLS is used, estimations of the regression will frequently turn out to be inefficient, inconsistent and biased, and such outcomes could well have incorrect impacts on health programs and policies. Methods: We considered three statistical methods for modelling the distribution of days of PA data for respondents in the 2013 Health Information Trends Survey (HINTS). The three regression models analyzed were: Ordinary Least Squares (OLS), Negative Binomial (NBRM), and Zero-inflated Negative Binomial (ZINB). We used the exact same predictor variables in the three models. Our results illustrate the differences in the results. Results: Our analyses of the PA data demonstrated that the ZINB model fits the observed PA data better than either the OLS or the NBRM models. The coefficients and standard errors differed in the zero-inflated count models from the other models. For instance, the ZINB coefficient for the association between income and PA behavior was not statistically significant (p>0.05), whereas in the NBRM and in the OLS models, it was statistically significant (p<0.05). Conclusions: The inappropriate use of regression models could well lead to wrong statistical inferences. Our analyses of the number of days of moderate PA demonstrated that the ZINB count model fits the observed PA data much better than the OLS model and the NBRM.

Purpose- The study investigates the effect of financial deepening on stock market performance in selected Sub-Saharan African countries by determining the relationship that exist between financial deepening and stock market performance. Expansion in the financial services to reach out to the underbanked or unbanked in our society enables these individuals to assess banking services, thereby boosting economic activities. Methodology- The study considers four selected countries in Sub-Saharan African over the period 2001 to 2019. Multiple regression analysis techniques were used with Seemingly Unrelated Regression (SUR) to analyse the data. SUR used in this analysis provides the lowest standard errors of the estimated parameters. Findings- Ordinary Least Square (OLS) gives consistent results. However, it is not as efficient as the SUR method, which amounts to feasible generalised least squares with a specific form of the variance matrix. It solves the problem of endogeneity. The study conducted Augmented Dicky Fuller (ADF) test, Hausman test, and Bruce Pagan test to avoid any challenges associated with data normality. Conclusion- The research finds out that broad money supply, a proxy for financial deepening, positively and statistically significantly impacted stock market performance in each of the four countries. It was recommended that all countries involved in this study and others implement policies that seek to enhance financial deepening in increasing broad money supply as a percentage of GDP. The increase in overall money supply allows for investment in productive sectors of the economy. Keywords: Sub-Saharan Africa, seemingly unrelated regression, Augmented Dicky Fuller, investigates, ordinary least squares. JEL Codes: E51, F21

Exports and investment play an important role in the economy of a country that is open. Exports will generate foreign exchange, foreign exchange to finance imports of capital goods and raw materials required in the production process that will create added value for the economy. The variables examined in this study is the difference in private sector investment plans, loan interest rates, government spending, and Gross Domestic Bruto (GDP). Research on the analysis using the method of least squares OLS (Ordinary Least Square). Results of regression test based on the theory, statistical and econometric criteria. The results of this study are interest rates significantly have a positive affect on the private investment. GDP will have a positive effect on investment and also government spending has a positive effect on private investment.

The success or failure of a government in organizing, managing and developing a country is shown by the rate of economic growth. However, many factors, both local and global, influence this. After experiencing a decline during the 1998 crisis, the Indonesian economy is now estimated to be starting to stabilize. At that time, economic growth was negative and inflation had soared to 80%. This research aims to analyze the influence of investment, exports, government spending and labor both simultaneously and partially in Indonesia during the 2001-2021 period. This research uses secondary data in the form of time series data for the period 2001-2021 in Indonesia. The analysis method used is Ordinary Least Square (OLS). The research results show that export and labor variables have a significant effect on economic growth, while investment and government spending have no effect on economic growth in Indonesia during the 2001-2021 period.