Ordinary Least-squares Method Research Articles

A Novel Interval Type-2 Fuzzy System Identification Method Based on the Modified Fuzzy C-Regression Model.

In this article, a novel interval type-2 Takagi-Sugeno fuzzy c -regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to describe the distance between each data point and the local type-2 fuzzy model. To improve the robustness of the proposed identification method, a modified objective function is presented. In addition, different from most previous studies that require numerous free parameters to be determined, an interval type-2 fuzzy c -regression model is developed to reduce the number of such free parameters. Furthermore, an improved ratio between the upper and lower weights is proposed based on the upper and lower membership function with each input data, and the ordinary least-squares method is adopted to establish the type-2 fuzzy model. The Box-Jenkins model and two numerical models are given to illustrate the effectiveness and robustness of the proposed results.

Heterogeneous Impact of Electrification of Road Transport on Premature Deaths from Outdoor Air Pollution: A Macroeconomic Evidence from 29 European Countries

One of the biggest problems associated with vehicles that use internal combustion engines is that they cause elevated levels of pollution in the places they travel through, especially if they cause congestion. However, it is not only the level, but also probably the concentration of gases emitted by internal combustion engines in the places where they move around that is particularly lethal. Can the road transport sector’s electrification mitigate premature deaths from outdoor air pollution? Our main hypothesis is that replacing internal combustion engine vehicles with electrical ones contributes to mitigating people’s exposure to high concentrations of air pollution. To answer the research question, a panel of 29 European countries, from 2010 to 2020, using the method of moments quantile regression and ordinary least squares, was examined. Results support the concept that economic growth, renewable energy consumption, and electric vehicles in all quantiles have a negative impact on premature mortality due to air pollution. These impacts are higher on premature mortality in lower quantiles, but gradually decrease with increasing quantile levels. The results also reveal that methane emissions, in all quantiles except 10th, have a negative effect on premature mortality. Nitrous oxide emissions positively impact premature mortality in all quantiles except the 10th, and this impact increases at high quantiles. Fine particulate matter positively impacts premature mortality in all quantiles, with the same at all levels. The ordinary least squares, used as a robustness check, confirm that economic growth, renewable energy consumption, and methane emissions have reduced impacts on premature mortality due to outdoor air pollution. However, nitrous oxide emissions and fine particulate matter increase premature mortality. These results reinforce the importance of policymakers implementing policies for road electrification.

An analysis of the success of fiscal adjustment in reducing public debt: Evidence from Pakistan.

This study seeks answers to questions such as: what is fiscal adjustment? Which fiscal strategy will result in a reduction in public debt liabilities? In the pursuit of answers to these questions, the study has defined two objectives. Firstly, the fiscal adjustment episodes must be identified in order to detach the discretionary fiscal stance; secondly, the success of these adjustment episodes in reducing public debt liabilities must be assessed. As a result, attempts will be made to undertake analyses that would simplify the issues underlying Pakistan’s practical policy options. A total of eleven adjustment episodes have been observed in a sample, ranging from 1976 to 2017, following Alesina and Ardagna’s definition. The descriptive analysis reveals that five episodes succeeded in reducing the public debt, while six episodes failed to reduce the ratio. Out of the five successful episodes, four are found to be spending-based and one is tax-based. To quantify the success of fiscal adjustment, the empirical model has been calibrated on Leibrecht and Scharler’s model and estimation is done via both the Ordinary Least Squares (OLS) and Robust Least Squares (RLS) methods. The RLS method produces better outcomes than the OLS method. Under RLS, all variables are significant except GDP growth, whereas in the OLS model, the election year and regime shift, together with GDP growth, are statistically insignificant. The fiscal adjustment’s composition reveals that spending-based consolidation boosts the chances of the fiscal adjustment’s success. Fiscal authorities should, therefore, adopt spending-based austerity measures to ensure the sustainability of public finances and prevent the negative macroeconomic consequences of unsustainable public debt.

Determinants of Youth Unemployment in the Democratic Republic of Congo: An Empirical Analysis from 2001-2020

This article examines the determinants of unemployment in the DRC. The reduced nature of the sample, namely one country, is explained by the availability of data covering the period 2001-2020.The model inspired by the empirical literature is estimated by the ordinary least squares (OLS) method. After estimates by the OLS method, our econometric results reveal that the variables education, inflation, direct investment abroad, the population significantly explain the level of youth unemployment at the threshold of 1% and 5%. Education and foreign direct investment tend to increase the unemployment rate while population growth and inflation tend to reduce the unemployment rate. Our analysis of the determinants of unemployment appears to contribute to the literature on several levels. First, because few studies exist on the subject in the Democratic Republic of Congo. Compared to existing work, our study goes beyond methodologically by relying on more robust techniques such as ordinary least squares (OLS) and instrumental variables. Finally, the article will serve as an interpellation and awakening of the consciences of political actors on the intolerable degree of unemployment in the context of the DRC, and will refer to it when necessary in political or economic decision-making.

NEXUS BETWEEN EDUCATIONAL ATTAINMENT AND INCOME IN KHULNA CITY: AN EMPIRICAL EXPLORATION

The economic development of any country not only depends on physical capital but also human capital. Education is an important and valuable factor of human capital development and overall improvement of society. This paper aims to examine the determinants of education and its effect on income level in Khulna city. The sample size is 250 service holders. Therefore, the study estimates are based on the method of ordinary least squares (OLS) and two stage least squares (2SLS). The results suggest that parents’ education is important factors of education attainment. Moreover, education and experience are positively correlated with the income. This study has recommended that the parents should be educated and conscious about the educational environment for their child. Moreover, it is essential to improve the cognitive ability, training facilities, institutional opportunities, result which also affect the income level. To get a good atmosphere, different education policies should be taken by the authority like – inclusive policy, mandatory higher secondary education and subsidy for students.

COMPARISON OF AUTOREGRESSIVE MODEL WITH MISSING DATA TREATED USING ORDINARY LEAST SQUARES AND INTERPOLATION WITH WEIGHTING METHOD

Bandung is committed to contributing to the achievement of the Sustainable Development Goals (SDGs) in Indonesia. One of the efforts that can be made to support the 13th pillar of SDGS regarding climate change is to forecast the air temperature of Bandung City in the future. One of the models that can be used for forecasting air temperature data in Bandung is the Autoregressive (AR) model. Based on BMKG data, often the time series data obtained has missing data. Therefore, in order to do a good time series analysis, it is necessary to make an effort to correct the missing data. The purpose of this research was to examine the procedure for overcoming missing data in the AR model using the Ordinary Least Squares (OLS) method and Interpolation with Weighting, which was applied to forecasting the average air temperature data in the city of Bandung. The research methodology followed the Box-Jenkins 3-step procedure. The first-order AR estimation parameter model was estimated using the OLS method and then used to overcome missing data using both methods with weighting using R software. Both methods resulted in an estimated value of 0.9991 and the same Mean Average Percentage Error (MAPE) value of 2,459% with very accurate criteria. Therefore, to overcome the missing data on the average air temperature data in the city of Bandung with a parameter estimator close to one, we got the same result for both methods.

A New Optimization Scheme for Robust Design Modeling with Unbalanced

The Lin and Tu (LT) optimization scheme which is based on mean squared error (MSE) objective function is the commonly used optimization scheme for estimating the optimal mean response in robust dual response surface optimization. The ordinary least squares (OLS) method is often used to estimate the parameters of the process location and process scale models of the responses. However, the OLS is not efficient for the unbalanced design data since this kind of data make the errors of a model become heteroscedastic, which produces large standard errors of the estimates. To remedy this problem, a weighted least squares (WLS) method is put forward. Since the LT optimization scheme produces a large difference between the estimates of the mean response and the experimenter actual target value, we propose a new optimization scheme. The OLS and the WLS are integrated in the proposed scheme to determine the optimal solution of the estimated responses. The results of the simulation study and real example indicate that the WLS is superior when compared with the OLS method irrespective of the optimization scheme used. However, the combination of WLS and the proposed optimization scheme (PFO) signify more efficient results when compared to the WLS combined with the LT optimization scheme.

The association between health expenditure, institutions, and economic growth in MENA countries.

Background: Expenditure on health is vital in the development of a country. Furthermore, the current COVID-19 pandemic emphasises the importance of health investments in maintaining a healthier economy worldwide. A substantial amount of empirical research on the relationship between health expenditure and economic growth yields conflicting results. The study intends to investigate the relationship between health spending and economic growth and institutions’ role in causing health spending to promote growth. Methods: The study uses longitudinal data to examine the relationship between health spending and economic growth in seven MENA countries from 2000 to 2017. The study uses the Phillips Perron (PP) Fisher chi-square stationarity test, indicating that the data series is not stationary. Following this, we used the Pedroni test for cointegration, and the results show long-run relationships between the variables. Next, Granger causality determines the direction of causality. Finally, panel data methods of panel ordinary least squares (Panel OLS), fully modified OLS (FMOLS), and dynamic OLS (DLOS) supplement the findings. Results: The Pedroni cointegration test (P value<0.0001) indicates that the variables have a long-run cointegrating relationship. On the other hand, the Granger causality test finds no causal relationships between health spending and economic growth. Furthermore, the panel data models show that expenditure on health does not directly contribute to higher economic growth in MENA countries. Conclusion: The findings of this study indicate that health spending does not lead to increased economic growth; this could be due to poor institutional quality. However, for health spending to positively impact economic growth, these investments in health care must be supplemented by other factors, particularly institutions.

Isobaric Vapor–Liquid Equilibria for Binary Mixtures of Adiponitrile + 2-Methylglutaronitrile and Adiponitrile + Trans-3-pentenenitrile at 5 kPa and 10 kPa

The isobaric vapor–liquid equilibria (VLE) of two binary mixtures (adiponitrile + 2-methylglutaronitrile and adiponitrile + trans-3-pentenenitrile) at 5 and 10 kPa were measured using a modified Rose–Williams still. The thermodynamic consistency of the VLE data was tested by the Wisniak method and the Fredenslund method. The isobaric VLE data were correlated with four activity coefficient models of Margules, van Laar, Wilson, and NRTL, and the parameters of the models were obtained by the ordinary least-squares method. The isobaric VLE data were compared with the original UNIFAC predictive model. Furthermore, the excess Gibbs free energy of studied mixtures was determined. The RMST and RMSy1 were determined to evaluate the regression results. The results show that the Wilson model has a better correlation effect than other models.

Forecasting the Chinese stock market volatility: A regression approach with a t-distributed error

ABSTRACT In this paper, we improve the ordinary least squares (OLS) estimation approach by replacing a normally distributed error with a t-distributed error. Empirically, we investigate the predictability of the Chinese stock market volatility based on this modified approach. Results show that the modified OLS method with a t-distributed error has a significantly stronger forecasting power than its counterpart with a normally distributed error. From an asset allocation perspective, the modified OLS approach can help a mean-variance investor obtain sizeable utility gains. We also conduct two extended empirical analyses and further verify the superiority of the regression approach with a t-distributed error. Our results are robust to a series of settings. Finally, we find that the regression approach with a t-distributed error shows greater tolerance for outliers by assigning smaller weights to them, thereby highlighting its superior performance.

Processing Sunshine Duration Measurements for the Assessment of Solar Radiation in Climatic Regions of the Central African Republic

Abstract The objectives of this work carried out in the Central African Republic are to propose new correlations between the components of solar radiation and the sunshine duration on a horizontal surface on the ground, and then to make an evaluation of the solar potential in the cities of Bambari, Birao, and Bangui. Polynomial regression models were used and their parameters were estimated by the ordinary least-squares method. A statistical evaluation allowed us to compare the performance of the models. The best correlations are then used to estimate the global and diffuse radiation. In the city of Birao, the estimated global radiation is around 6 kW h/m2 j and the diffuse radiation is around 2 kW h/m2 j; in Bambari, the global radiation is around 5.4 kW h/m2 j and the diffuse is around 2.3 kW h/m2 j; in Bangui, the global radiation is around 5 kW h/m2 j and the diffuse radiation is around 2.3 kW h/m2 j. The potential solar in all these regions is very favorable for small- and large-scale solar photovoltaic applications.

Farmer Heterogeneity and Land Transfer Decisions Based on the Dual Perspectives of Economic Endowment and Land Endowment

Guiding qualified farmers to transfer their land is an important way to alleviate the problem of land abandonment, improve land use efficiency, and achieve moderately large-scale land management. Based on the dual perspectives of economic endowment and land endowment, this paper uses the 2015 data of the China Household Finance Survey (CHFS), using the semilogarithmic ordinary least-squares method and the logit model, to explore differences in land transfer decisions under the effect of farmer heterogeneity. The circulation trading market was further improved to provide a reference. The heterogeneity of economic endowment and land endowment significantly affects the decision-making behavior of farmers in transferring land. The higher the land endowment is, the greater the probability that farmers transfer the land out and successfully trade, and they are more inclined to transfer the cultivated land to cooperatives, village collectives, and other institutions through formal channels, leading to a higher unit income of the transfer. Further research shows that land endowment has no significant difference in the impact of land endowment on whether farmers with different livelihood endowments transfer their land, but under the same land endowment, farmers with economic endowment advantages are more able to use their own endowment advantages to transfer their land out through formal channels and obtain higher gains income. Therefore, focusing on improving the conditions of land resources and increasing the endowment of farmers are important means to promote successful transactions in the land transfer market, ensure its sustainable operation, and promote further increase in the income of transfer farmers.

Implementing Anti-money-laundering Goals: New Technologies or Coordination between Related Agencies?

ABSTRACT This paper is to investigate whether anti-money-laundering (AML) improvement in a gaming tourism destination is driven by the integration of new technologies, or by other factors such as financial inclusion and close coordination between companies and related government bodies. The quantitative method of ordinary least squares (OLS) multivariate regression is applied to determine the degree to which money-laundering activities in Macau reduced between 2007 and 2017. This study reveals a strong positive relationship between the number of suspicious transaction reports (STRs) and the amount of private-sector domestic credit as a percentage of GDP. The relationship between investment in IT as a percentage of GDP and the number of STRs is statistically insignificant, but there is a positive association with the number of STRs in the gaming tourism industry. Close coordination between industry players and various AML agencies has also had a positive impact on STR detection. Besides, this study will be of benefit to the gaming tourism destination, showing that casinos can cooperate openly with financial institutions and government bodies to fight money laundering, which will help to ensure that gaming tourism destination maintains a positive image sustainably.

Financing Constraints and Firm's Productivity Under the COVID-19 Epidemic Shock: Evidence of A-Shared Chinese Companies.

Focusing on the financing barriers to firm productivity improvement under the influence of external shocks, we empirically analyze the data of A-share listed companies from 2007-2018 to determine the impact of financing constraints on total factor productivity (TFP) in the context of COVID-19 pandemic and the paths of factor use efficiency and R&D innovation efficiency on this impact using ordinary least-squares (OLS) method. We find that financing constraints are an important factor inhibiting the TFP of firms. This inhibitory effect is more serious in small-scale firms, non-state firms, and non-energy firms. Further investigation shows that the inhibitory effect of financing constraints on firms’ TFP is more pronounced when firms are located in the Yangtze River Delta city cluster, the Pearl River Delta city cluster, non-port cities, and provincial capitals. The mechanism test finds that improving the efficiency of capital use and labor use can alleviate the suppressive effect of financing constraints on TFP. The alleviating impact is more significant when capital use efficiency is improved. However, increasing the efficiency of R&D innovation further strengthens the inhibitory effect of financing constraints, and this effect is more pronounced under positive external shocks.

Quantitative Effects of Climate Change on Vegetation Dynamics in Alpine Grassland of Qinghai-Tibet Plateau in a County

Alpine grassland in the Qinghai-Tibet Plateau is known to be sensitive to climate change. To quantify the impacts of climate change on alpine ecosystems at small scale, Wulan County in Qinghai Province was taken as the research object, and the relationships between vegetation dynamics and climate changes and the direct and indirect effects of climate factors on vegetation dynamics were analyzed using the methods of ordinary least squares, Pearson correlation analysis and path analysis, on the basis of MOD13A3 data and meteorological data from 2001 to 2020. The results showed that the Normalized Difference Vegetation Index (NDVI) in the growing season of the county and 5 vegetation types showed similar fluctuation processes and relationships with climate factors during the study period. The growing season NDVI (GSN) of shrubland and desert steppe respectively were the highest and lowest. The yearly mean values of GSN over the county ranged from 0.151 to 0.264, and increased extremely significantly with years at a rate of 0.0035 yr−1. Spatially, GSN gradually decreased from northeast to southwest, and 97.2% of the county area showed an increasing trend in GSN. With years, growing season evaporation (GSE) decreased extremely significantly at a rate of 29.6 mm yr−1, while growing season average relative humidity (GSARH) showed a significant increasing trend at a rate of 0.16% yr−1. The correlations and effects of GSE, GSARH, and growing season precipitation (GSP) on vegetation dynamics were weakened in turn. GSE was the main direct effect factor, and the latter two were the indirect effect factors through GSE. The total contribution rates of GSE, GSARH and GSP to vegetation dynamics was about 78.0%. However, growing season average temperature (GSAT) had little effect on vegetation dynamics. This study provides information for understanding the characteristics of vegetation dynamics of alpine grassland in the Qinghai-Tibet Plateau at a small scale.

Utilisation of spaceborne C-band dual pol Sentinel-1 SAR data for simplified regression-based soil organic carbon estimation in Rupnagar, Punjab, India

Soil Organic Carbon (SOC) is a measure of the total carbon content of the soil and is a vital soil health indicator. Over the decades, SOC has been estimated using sampling followed by rigorous laboratory-based testing methods. Spaceborne Microwave/Synthetic Aperture RADAR (SAR) remote sensing has proven to be a versatile tool for various soil study applications. However, there have been very few studies conducted for SOC estimation using SAR remote sensing. This study utilises time-series, C-band remotely sensed SAR data from Sentinel-1 A satellite for SOC estimation and compared the performances of Random Forest (RF) and Ordinary Least Squares (OLS) Regression models over agricultural areas of Rupnagar district of Punjab in India. A set of 96 soil samples were collected from 32 different agricultural field locations in Rupnagar district between November 2019 to January 2020. SAR backscatter of Vertically emitted and Vertically received (VV) and Vertically emitted and Horizontally received (VH) polarisation channels, from Sentinel-1, soil moisture, electrical conductivity, pH, temperature and SOC from the laboratory-based testing methods were used as regression parameters. The RF regression gave a Root Mean Square Error (RMSE) of 0.78 and R2 statistics of 0.887, while the OLS method performed better with an RMSE of 0.53 and an R2 value of 0.907. It was also observed that the backscatter from VV and VH polarisation channels, when used synergistically with field data, have the highest Feature Importance (FI) score in both RF and OLS regression models for SOC estimation.

Daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere

Over the past 50 years, global land surface air temperature has been rising at a much higher rate at night than during the day. Understanding plant responses to the asymmetric daytime and nighttime warming in the context of climate change has been a hot topic in global change biology and global ecology. It has been debatable whether the asymmetric warming has opposite effects on vegetation activity (e.g., phenology, productivity). Here we settle the debate by scrutinizing the underpinnings of different statistical methods and revealing how the misuse or improper use of these methods could mischaracterize the effects of asymmetric warming with in situ and satellite observations. The use of the ordinary least square (OLS) methods including both daytime (Tmax) and nighttime (Tmin) temperature in the multiple regression models could overlook the multicollinearity problem and yield the misinterpretations that Tmax and Tmin had opposite effects on spring phenology, autumn phenology, gross primary production (GPP), and normalized difference vegetation index (NDVI). However, when the OLS methods were applied with Tmax and Tmin included in separate models or alternatively the ridge regression (RR) method with properly selected ridge parameter was used, the effects of Tmax and Tmin on vegetation activity were generally in the same direction. The use of the RR method with improperly selected ridge parameter could also mischaracterize the effects of asymmetric warming. Our findings show that daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere, and properly dealing with the multicollinearity problem is critical for understanding the effects of asymmetric warming on vegetation activity.

Dynamic parameter identification and nonlinear friction compensation method for safety perception of heavy explosion-proof robots

This article focuses on the problem of how to accurately calculate the joint control torques when the explosion-proof robot performs collision detection without sensors and gives a complete solution. Nonlinear joint frictions are incorporated into the dynamic model of a robotic manipulator to improve calculation accuracy. A genetic algorithm is used to optimise the excitation trajectories to fully stimulate the robot dynamic characteristics. Effective and applicable data filtering and smoothing methods are proposed and the Iteratively Reweighted Least-Squares method based on the error term is applied to identify the robot dynamic parameters. Compared with Ordinary Least-Squares method, the proposed algorithm improves the accuracy of joint control torques estimation.

Human Immune Virus (HIV) Infection among Children, Treatment and Under-5 Deaths in Nigeria: An Empirical Analysis

Purpose: This study investigates the impact of Human Immune Virus (HIV) infections among children and under-5 deaths in Nigeria. Since the prevalence of HIV infection, Nigeria health care and the government has experienced many challenges towards achieving healthy outlook among the citizens and this struggle has continued over the years, with the consideration of the thunderous increase of the under-5 deaths caused by HIV infections in Nigeria. Therefore, this study investigates HIV infections, its treatment and under-5 deaths in Nigeria and contributes a way forward towards recuperation in achieving a healthy society among Nigerians. Design/Methodology/Approach: This study used the Ordinary Least Square (OLS) method to investigate the impact of HIV infections among children on under-5 deaths in Nigeria. Being an empirical study, the data used for its analysis were annual secondary data and they were obtained from World Bank indicators which is a reliable source. Findings: The findings from the result of the OLS method of analysis prove that HIV infections increase under-5 deaths at 1% significant level in Nigeria. The result also proved that treatment of HIV victims definitely reduces under-5 deaths in Nigeria at 1% significant level.

An Efficient Method for Hyperbolic-Based Localization in SAR RFID Systems

Radio frequency identification (RFID) is one of the major wireless communication technologies and has been applied in the supply chain and retail network for a long time. By contrast, the application of RFID in localization emerges in recent years. Besides, neither precision nor real time of existing RFID localization methods is satisfactory enough. In this article, we propose a novel method called HyLoc for localization of RFID tags based on the hyperbolic model. The problem to locate RFID tags is modeled as the solution of hyperbolic equations formed by synthetic aperture radar (SAR). To solve the equations quickly, the linear least-squares (LLS) algorithm is first used to find the approximate solution, and then, the estimation error is corrected by the Taylor series method for higher accuracy. Comparisons of performance with the state-of-the-art methods in SAR RFID localization are carried out through simulations and experiments. The results show that the proposed method is closer to the Cramér–Rao lower bound than the holography method at high noise level and more efficient than the ordinary nonlinear least-squares (NLS) method without loss of accuracy.