Ordinary Least-squares Method Research Articles

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even-aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper-parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 – 2.11 m, and the relative RMSE was less than 10 % (7.68 – 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden.

Effect of government intervention in relation to COVID-19 cases and deaths in Malawi

The COVID-19 pandemic affected the world in various ways. In response to this, countries set up various interventions such as lockdowns, physical distancing, and mandatory face covering, among others. Governments also put in place measures to ensure compliance. However, the extent to which the various responses impacted the deaths and confirmed cases remains debatable. This paper explores this question by looking at how government stringency measures impacted deaths and cases in Malawi. We employ an instrumental variable (IV) approach to assess the impact of government action on confirmed COVID-19 cases and deaths. We measure government policy by the stringency index. By leveraging the IV approach, we circumvent potential endogeneity concerns between our main policy variable and the outcome variables. Our data comes from the University of Oxford COVID-19 project and spans a daily frequency from 20 February 2020 to 25 April 2022, covering multiple waves of the pandemic. Overall, the findings show that despite Malawi never having implemented a full lockdown, the government policies may have helped to reduce both cases and deaths related to COVID-19. Specifically, the IV shows that a unit increase in the government stringency index results in a drop of 179 cases and 6 deaths. All the results are statistically significant at 1% level and remain robust to the use of the ordinary least-squares method. This study demonstrates the efficacy of non-pharmaceutical tools implemented by the government to fight COVID-19. Thus, policymakers need to place more emphasis on the need for the public to adhere to these stringency measures in the event of new waves of the pandemic or similar outbreaks.

The Impact of Female Directors on the Relationship Between Corporate Social Responsibility and Capital Structure: Evidence From Egypt

This paper examines how corporate social responsibility affects capital structure. The sample consists of 58 non-financial companies listed on the Egyptian Stock Exchange, with 464 firm-year observations made between 2014 -2021 using the generalized method of moments and ordinary least squares. Data was gathered from reports and financial statements from an Egyptian information dissemination firm. This paper demonstrates that firms with female directors explain the negative impact of the association between corporate social responsibility and capital structure. Female directors and CSR disclosure improve manager oversight and reduce information asymmetry and agency conflicts. Results support agency and gender theories that expect different behavior from women in leadership posts.

An empirical study of Credit Policy Determinants of Algerian Banks/ During the period (1989-2009)

The study aimed to test the effect of size of deposits, the real interest rate, population, economic reforms on the credit policy of commercial banks of Algeria represented by the size of credit facilities. The study Used annual data for the period from 1989 to 2009. Where the Multi regression model was estimated using a method of Ordinary Least Squares OLS.Results of the analysis showed that there is a positive relationship with a statistically significant between the credit facilities and the deposits and the variable of economic reforms, while the real interest rate and population size have been linked by significant negative relationship

Bayes' approach of linear regression to modeling the human development index in Indonesia

Regression analysis is one of the data analysis techniques that can be used to examine the correlation between two variables, namely dependent variable and independent variable. It’s can be used to determine the parameter estimation of linear regression models are; the method of least squares or ordinary least square (OLS), Maximum likelihood estimation (MLE), and the Bayes method. Bayes' method defines the parameter as a random variable that describes the initial comprehension of the parameter before the observation was initiated and elucidated in an initial distribution refer as the prior distribution. The prior distribution used in this study is the pseudo prior distribution. The Data used in this study is secondary data, namely human development index (HDI) data in 2020, which was obtained from the website of the Central Statistics Agency (BPS). This study aims to estimate the regression model parameters using the Bayes method on the HDI data and the population data which adepts with information and communication technology (ICT) in Indonesia in 2020. The results of the specimen and analysis showed that population variables with ICT adepts have a significant effect on HDI variables. The results of the determination coefficient showed that 78.42% of HDI variables are affected by the population variables with ICT adepts while the remaining 21.58% are affected by other factors that have not been studied.
 Keywords: Bayes method, human development index, information communication and technology, linear regression, pseudo prior.MSC2020: 62F15

The board of directors influence on the information quality of financial reporting through accounting conservatism – Empirical evidence on Vietnamese listed enterprises

The information quality contained in financial reporting has practical implications for stakeholders. Accounting conservatism is a criterion that affects the authentication of the value of assets and liabilities related to the financial reporting of enterprises. The role of the board of directors is to supervise the information quality of an enterprise. The purpose of the paper is to examine how the board of directors influences the information quality of financial reporting through accounting conservatism. The survey sample includes 100 listed enterprises that have the highest capitalization in Vietnam’s stock market. Time series data taken for the last five years are published by enterprises from 2018 to 2022. The paper implements the quantitative method of ordinary least squares to test the hypotheses. The results explore that board size, board independence, and audit organization affect the information quality of financial reporting through accounting conservatism. Accordingly, board size has the strongest influence, and board independence has the weakest effect on the information quality of financial reporting through accounting conservatism. The research suggests some policies for Vietnamese listed enterprises to have appropriate regulations for the board of directors and strengthen control of the information quality of financial reporting.

Validation of the EPACODI-1 Scale: University Students' Perceptions of Inclusive Education.

In the last fifteen years, changes have been taking place in education systems at the international and national levels that aim to achieve, in the near future, the objectives set by the UN International Convention on the Rights of Persons with Disabilities (hereafter CRPD). Spain is no stranger to these objectives, as recognized in the new Organic Law for the Modification of the Law on the Organization of Education. This situation makes it necessary to know the perceptions that professionals in training (about to graduate) from faculties of education have about the legal content established by articles 4.1.i and 24 of the CRPD on inclusive education. In order to ascertain these perceptions, the EPACO-DI-1 instrument was used in a quantitative and confirmatory study by means of a multivariate factor analysis (CFA), applying the parallel estimation method of ordinary least squares (OLS) and principal axes with polychoric correlation and promax oblique rotation. This study involved 552 fourth-year students between the ages of 21 and over 45 from the specializations offered by the Faculty of Education of the University of Murcia. The obtained results are consistent and show the validity of the EPACODI-1 scale for determining the perceptions of professionals in training on inclusive education.

Analysis of Urbanization and Energy Consumption Using Time Series Data: Evidence from the SAARC Countries

Urbanization has posed some tremendous challenges which are related to environmental stresses through increased energy consumption. These challenges have drawn attention to the need to implement urbanization with sustainable energy consumption globally. The present study aims to identify the urbanizing factors that cause energy consumption in the SAARC countries. The South Asian Association for Regional Cooperation is considered in the study during the period of 1975-2014. The data are analyzed by using simple statistics and econometric techniques, such as the ordinary least squares (OLS) method for the country level. The study has found that all urbanizing variables significantly affect energy consumption with different levels in different countries, as shown by the OLS method. The coefficient of GDP is statistically significant at 1% level of significance for Bangladesh, Pakistan and Sri Lanka, while at 5% and 10% levels for India and Nepal, respectively. The coefficient of the industrial sector share in GDP is statistically significant at 1% level of significance for Bangladesh, Nepal and Pakistan. The result shows that a 1% increase in the service sector’s share in GDP leads to a reduction in energy consumption of 0.15%, 0.34% and 1.61%, respectively, in Bangladesh, Nepal and Sri Lanka. The result for urban population indicates that a 1% increase in urban population leads to an increase in energy consumption by 1.94%, 2.32%, 0.85% and 3.87%, respectively, for Bangladesh. India, Nepal and Sri Lanka. Green technology and energy efficiency technologies to use in the industries, encourage using public transportation, sustainable energy and urbanization are potential policy recommendations.

Underpricing of IPOs (Initial Public Offering) in Borsa Istanbul: The effect of Covid-19 Pandemic Period

Purpose- This research has both theoretical and practical implications. The study's findings will provide valuable insights into the impact of the COVID-19 pandemic on IPO mispricing in the Turkish IPO market. The study delves into the behavior of IPOs that were issued between 2010 and 2022, with a focus on how they were affected by the COVID-19 pandemic. The results may help investors and issuers understand the pandemic's effects on IPO pricing and inform their investment decisions. The study will also provide valuable insights to investors, regulators, and market participants, allowing them to make more informed decisions during market volatility and uncertainty periods. Methodology- The study involves employing two methods: the traditional ordinary least squares (OLS) and the more appropriate quantile regression (QR). The OLS method focuses on assessing the average impact of independent variables on mispricing, disregarding the unexplored latent characteristics of the mispricing distribution, especially when it deviates from a normal distribution. In contrast, the QR method allows us to investigate the diverse effects of independent variables at different levels of mispricing due to the asymmetric distribution of returns. By employing the QR approach, it can be identified the specific impacts of each variable on IPOs within particular levels of mispricing. This robust method is capable of handling potential heterogeneity in the distribution, which was observed in the sample. The QR method also facilitates the examination of various segments of the mispricing distribution, including the tail regions, enabling a comparison of the effects of explanatory factors on IPOs that range from extremely overpriced to extremely underpriced. Due to the constraints of the project, the paper has been done with a limited number of shares. Findings- The study investigates the impact of the COVID-19 pandemic on Initial Public Offering (IPO) mispricing in financial markets, examining changes in mispricing levels before, during, and after the pandemic. The results indicate a significant increase in IPO mispricing during the COVID-19 period, consistent with expectations due to factors such as heightened asymmetric information, reduced IPO volume, and decreased demand. The analysis tests the hypothesis that Covid-19 has a significant impact on IPO results. The results show that the null hypothesis (H0) cannot be rejected, supporting the notion that the pandemic has a substantial effect on IPO mispricing. This is particularly evident in equations examining 1-year returns. Furthermore, the study explores the influence of various factors on IPO mispricing, including stock market indices and dummy variables representing different years. While some index values are found to be insignificant, the Borsa Istanbul-All Index and dummy variables for 2020, 2021, and 2022 are significant in specific equations. Notably, the persistence of the impact of COVID-19 beyond the relevant period suggests a lasting effect on IPO mispricing. Examining sector-specific effects, the study finds that, based on first-day returns, all sector values, except for SEC 3 (Consumer Non-Cyclicals), are significant. However, for 1-year returns, only SEC 5 (Financials) and SEC 4 (Energy) exhibit significance, with the latter being slightly above the 10% limit. The increasing demand for energy companies in recent times is identified as a potential driver for higher levels of "IPO underpricing" in specific IPOs within these sectors. Conclusion- The study provides robust evidence of increased IPO mispricing during the COVID-19 pandemic, highlighting the persistent impact of the crisis on financial markets, as well as sector-specific nuances influencing mispricing levels. Keywords: IPOs (Initial Public Offerings), mispricing, pandemic, initial returns, long-term returns JEL Codes: C21, C23, D81, E44, G14

Evaluation of the Impact of Regional Trade Agreements on Indian Exports

This article scrutinises the repercussion of the regional trade agreements on Indian exports. The gravity model has been used for the analysis of two decades of panel data from 30 countries, spanning from 2001 to 2019. The gravity equation is estimated by the Ordinary Least Square (OLS) and Poisson pseudo-maximum likelihood (PPML) techniques using panel data regression. The PPML approach addresses the heteroskedasticity bias that is typically present in the OLS method. The study has revealed that the PPML results are more encouraging than those obtained based on the traditional OLS approach. According to the empirical findings, India’s trading partners in South Asia benefit more from its economic integration than India does. However, the measurement of country-specific degrees of globalisation has a negligible effect on fostering trade across select nations. JEL Codes: C1, F1, F2, F6

Application of the method of ordinary least squares in the calibration of temperature, pressure and mass meters

The goal of this paper is applied the Ordinary Least Squares (OLS) method as a strategy in order to reduce the uncertainty measurement associated to the relevant physical quantities. Methodology or method: This work was motivated due to the efforts made in the measurement sciences to investigate alternative methods that allow obtaining greater metrological reliability of the results, that is, reducing the uncertainty associated with the measurement. Thus, the applied methodology consisted of the development of a computational code using the MatLab tool, in which an algorithm was programmed that supports the application of the OLS method. Three of the most used physical magnitudes at an industrial level were evaluated: temperature, pressure and mass. These magnitudes were evaluated from the calibration of three measuring instruments: thermistor, manometer and digital scale. Results: The applied methodology allowed to: (i) obtain the matrix of the coefficients for polynomials of degrees 1, 2, 3 and 4 that adjust the experimental data; (ii) to draw the calibration curves for each of the obtained polynomials and (iii) to estimate the fit uncertainty (i.e.: the mean squared deviation) to specify the polynomial that best models the experimental data for a reliability level of 95.45 % (k = 2). The consolidated results confirmed a reduction in the uncertainty associated with the adjustment polynomial of 99.8% for the temperature measurement, 45.6% for the pressure measurement and 53.9% for the mass measurement. Conclusions: Finally, a discussion of the results is presented, confirming the effectiveness of the ordinary least squares method as a strategy to reduce the uncertainty associated with the calibration of measuring instruments.

A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels

The bidirectional reflectance distribution function (BRDF) of the land surface contains information relating to its physical structure and composition. Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies. However, the original kernel-driven models, which many operational BRDF/Albedo algorithms have adopted, do not explicitly consider the heterogeneity within heterogeneous pixels, which may result in large fitting residuals. In this paper, we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares (DWLS) inversion approach. The performance of DWLS and the traditional ordinary least squares (OLS) inversion approach were compared using simulated data. We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations. The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels. The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands. The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations. These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.

Research on the Development Mechanism of In-situ Urbanization Driven by Industry--Evidence from 87 cities in central China

Some problems caused by traditional urbanization are becoming prominent. Compared with traditional urbanization, the rural population does not migrate to large and medium-sized cities in the process of in-situ urbanization. Rural residents realized the transition from traditional to modern production and lifestyle in their native land, which avoids the problems of ‘rural diseases’ and ‘urban diseases’ in the process of traditional urbanization. This study explores the influence mechanism of three industries on in-situ urbanization through the System Generalized Method of Moments and Ordinary Least Square. We found that (1) The simple development of agriculture does not directly promote in-situ urbanization. The process of agriculture driving in-situ urbanization needs to separate some non-agricultural industries from agriculture, which could accelerate the in-situ transfer of rural labor force for non-agricultural employment. (2) The secondary industry plays a significant direct role in driving the in-situ urbanization. Simultaneously, it is conducive to promoting the in-situ urbanization under the joint action of industry and agriculture. (3) The influence of the tertiary industry on in-situ urbanization is mainly reflected in the rural service industry and rural tourism. Through the combined development of agriculture, service industry and tourism, rural residents could achieve local non-agricultural employment and civic lives.

Empirical Analysis of the Relationship Between Financial Development and ICT Infrastructure Development in Sub-Saharan Africa.

This study investigates the impact of financial development on information and communication technology (ICT) in sub-Saharan Africa. The analysis utilizes a panel dataset comprising 48 countries from the region and employs the bootstrap ordinary least squares (OLS) technique to ensure accurate and robust results. The findings reveal a significant and positive relationship between financial development and ICT indicators. Panel unit root tests indicate a combination of non-stationary behavior and stationarity after differencing the variables. Panel cointegration tests confirm a long-run, continuous cointegration between financial development and ICT infrastructure. The empirical results demonstrate significant cointegration between financial development and various ICT indicators, including internet usage, mobile subscriptions, telephone subscriptions, and fixed broadband subscriptions. The bootstrap OLS method further reveals that financial development has a statistically significant impact on the development of these ICT indicators. The results are robust, as confirmed by the Driscoll-Kraay standard errors regression. Based on these findings, policy recommendations are provided to enhance the synergy between financial development and ICT in sub-Saharan Africa, including promoting financial inclusion, investing in ICT infrastructure, strengthening financial sector regulations, fostering public-private partnerships, investing in digital skills and education, and supporting entrepreneurship and innovation.

Elastic Net Penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection

In quantile regression models, numerous penalization methods have been developed to deal with ordinary least-squares method problems. Such methods are ridge penalized quantile regression, lasso penalized quantile regression, and elastic net penalized quantile regression which are used for variable selection and regularization and deals with the multicollinearity problem when it exists between the predictor variables. However, the variables of interest are often represented through time series processes, in which such time series data are often non-stationary and non-linear, which leads to poor accuracy of the resultant regression models and hence results with less reliability. The EMD-EnetQR method is proposed to address this issue, which consists of applying the empirical mode decomposition (EMD) algorithm to time series data and then using the resulting components in penalized quantile regression models. This study aims to apply the proposed EMD-QREnet method to determine the influence of the decomposition components of the original time series predictor variables on the response variable to build a model fit and improve prediction accuracy. Furthermore, this study addressed the multicollinearity between the decomposition components. Simulation studies and real dataset applications were conducted. The results show that the proposed EMD-QREnet method, in most cases, outperforms the other methods by improving prediction accuracy.

낙뢰 분포와 배전용 변압기 뇌해고장의 영향에 관한 지리가중회귀분석

Distribution transformers are known to be affected by not only direct lightning strikes but also induced lightning induced overvoltage due to their relatively low insulation strength to withstand lightning strikes. In this regard, the purpose of this paper is to implement an explanatory model between the lightning strikes and the lightning failures of distribution transformers. Since spatial distributions are hard to be analyzed by the ordinary least square (OLS) method, geographically weighted regression (GWR) analysis was applied to find the explanatory properties between the spatial lightning strikes and the spatial lightning failure transformers. As a result of the regression analysis, GWR analysis shows improved explanatory power compared to the OLS method and enables additional analysis to be performed by specifying points showing high explanatory power and regression coefficients in the visual analysis.

Mapping algorithms for predicting EuroQol-5D-3L utilities from the assessment test of chronic obstructive pulmonary disease

To predict 3-Level version of European Quality of Life-5 Dimensions (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT), the study attempts to collect EQ-5D-3L and CAT data from COPD patients. Response mapping under a backward elimination procedure was used for EQ-5D score predictions from CAT. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates. Afterwards, the predicted scores were transformed into the utility. The developed formula was compared with ordinary least squares (OLS) regression models and models using Mean Rank Method (MRM). The MLR models performed as well as other models according to mean absolute error (MAE) and root mean squared error (RMSE) evaluations. Besides, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in the OLS method was improved through the means of the MLR model based on bubble chart analysis. In conclusion, response mapping with the MLR model led to performance comparable to the OLS and MRM models for predicting EQ-5D utility from CAT data. Additionally, the bubble charts analysis revealed that the model constructed in this study and MRM could be a better predictive model.

Does Regulation Matter? Assessing the Long-run Impact of Regulation on the Cost Efficiency of the Microfinance Institutions in Bangladesh

Bangladesh started regulating microfinance in 2006 by establishing the Microcredit Regulatory Authority, which granted licenses to 746 microfinance institutions (MFIs) and supervised them through prudential and non-prudential regulations. Economic regulation is relatively inexpensive. It affects performance which is mostly reflected in the costs and revenues of the MFIs. This paper investigates the relationship between cost efficiency and regulation using data from 534 MFIs. Cost efficiency was measured using the stochastic frontier model and the ordinary least-squares method to find the inefficiency determinants. It was observed that microcredit regulation reduced the cost-inefficiency of the MFIs significantly in Bangladesh.

Autoregression of Bilateral Trade Flow Matrices

Amongst a considered set of trading partners, does proportional bilateral trade flow in matrix form have autoregressive predictability? In this paper, the standard method of ordinary least squares (OLS) regression is modified for the input and prediction of matrices representing proportional exports of trading partners. Considering a small sample of trading partners and using the matrix regression model proposed, we find some evidence of autoregressive predictability. This predictability can be employed to predict future trade flows among different countries. Our findings provide some potential policy implications on international trade.

Prediction of the solubility of organic compounds in high-temperature water using machine learning

The estimation of the solubility of organic compounds in high-temperature water is important for designing chemical processes. This study aimed at predicting the solubility of organic compounds in high-temperature water in the range of 100–250 °C using machine learning. The chemical structure of the organic compound was converted into 196 descriptors (parameters) using an open-source toolkit. The experimental solubility data were regressed using the descriptors, temperature, and water density. The regression methods of ordinary least squares, least absolute shrinkage and selection operator (Lasso), and support vector regression (SVR) were compared. A regression method combining the Lasso and SVR (Lasso + SVR) was developed. The model thus obtained this method was found to accurately predict the solubility of organic compounds in high-temperature water, with a root-mean-square error of 0.5. The findings in this study would be useful for predicting the solubility of any organic compound in high-temperature water.