OLS Estimates Research Articles

Environmental kuznets curve and causal links between environmental degradation and selected socioeconomic indicators in Bangladesh.

This study has investigated the Environmental Kuznets Curve (EKC) hypothesis and focuses on the resource stock of the country in relation to the selection of environmental degradation indicators. Acknowledging the critical role of the EKC for policy formulation and development strategies, this study has examined the validity of the EKC hypothesis by exploring the relationship between economic growth, urbanization, energy consumption, trade openness, human capital and ecological footprints for the period 1972–2018 in Bangladesh. The autoregressive distributive lag bounds testing approach is applied for this purpose, taking into account the structural break in the time series. A fully modified OLS estimation has also been applied as the robustness check of the results. Results from the empirical analysis confirm the existence of the EKC in Bangladesh in both the long run and short run. The causal nexus among the variables is examined by applying the Vector Error Correction Granger causality test. The causality test result shows that economic growth and urbanization cause ecological footprints in both the short run and the long run. Based on this result, it can be inferred that economic growth activities in Bangladesh can be continued and extended with minimal ecological cost through structural economic change and proper environmental management.Graphic abstract

Ridge parameter estimation for the linear regression model under different loss functions using T-K approximation

In multiple linear regression models, the explanatory variables should be uncorrelated within each other but this assumption is violated in most of the cases. Generally, ordinary least square (OLS) estimator produces larger variances when explanatory variables are highly multicollinear. So, in this paper, we propose some new ridge parameters under Bayesian perspective relative to different loss functions, using Tierney and Kadane (T-K) approximation technique to overcome the effect of multicollinearity. We conduct the simulation study to compare the performance of the proposed estimators with OLS estimator and ordinary ridge estimator with some available best ridge parameters using mean squared error as the performance evaluation criterion. A real application is also consider to show the superiority of proposed estimators against competitive estimators. Based on the results of simulation and real application, we conclude that Bayesian ridge parameter estimated under general entropy loss function is better as compared to the OLS estimator and ordinary ridge estimator, when explanatory variables are small. This statement is also true for larger explanatory variables with small sample size. While for larger sample sizes and explanatory variables, the ordinary ridge estimator with best ridge parameter gives the better performance as others.

Effective investment in women's futures: Schooling with learning

Women's schooling has long been regarded as one of the best investments in development. Yet countries vary widely in the extent to which a year of schooling conveys useful skills and knowledge and hence schooling and education cannot be treated as synonyms. Using two different cross-nationally comparable data sets which contain measures of schooling, assessments of literacy, and life outcomes for more than 50 countries we show the association of women's education, defined as schooling and the acquisition of literacy, with four life outcomes (fertility, child mortality, empowerment, and financial practices) is much larger than the standard estimates of the gains from schooling alone. In our preferred instrumental variables (IV) estimates, that correct for the attenuation bias induced by measurement error, the gain from education is more than 3 times larger than the standard OLS estimates of gains from schooling alone. While our results are not causal estimates, if anything like our results are true in the causal pathways whereby schooling and learning lead to improvements in women's well-being this implies the estimates of the costs and benefits of expansions in grade attainment versus improving learning need to be adjusted accordingly.

The Academic Impacts of 2015 Nepal Earthquake: Evidence from Two Secondary Schools in Sindhupalchok District

How do natural disasters affect academic performance? Despite numerous studies having been conducted after the 2015 Nepal earthquake, the academic impacts of this tragic event have rarely been explored. Applying the OLS estimation on the microdata collected through a questionnaire survey among 189 secondary school students of two secondary schools in one of the hardest-hit rural villages, we found that students’ average annual test scores dropped by 7% after the earthquake. Human losses measured by the incidence of death or injury and economic losses proxied by the level of house damage in a respondent’s family were found to be significant in the decline in the annual test scores of the respondents. Because secondary schools are usually not very close for most of the students in rural mountainous communities, we controlled for time taken to reach school, which was also found to significantly increase the magnitude of the drop in the test score. However, students’ level of happiness measured using the Oxford Happiness Questionnaire (OHQ) and the mother’s level of education were found to be significant in reducing the magnitude of the drop in the test score of students. The findings suggest more support is needed for students who faced higher levels of human and economic loss in their family.

Weighted least squares estimators for the Parzen tail index

Estimation of the tail index of heavy-tailed distributions and its applications are essential in many research areas. We propose a class of weighted least squares (WLS) estimators for the Parzen tail index. Our approach is based on the method developed by Holan and McElroy (J Stat Plan Inference 140(12):3693–3708, 2010). We investigate consistency and asymptotic normality of the WLS estimators. Through a simulation study, we make a comparison with the Hill, Pickands, DEdH (Dekkers, Einmahl and de Haan) and ordinary least squares (OLS) estimators using the mean square error as criterion. The results show that in a restricted model some members of the WLS estimators are competitive with the Pickands, DEdH and OLS estimators.

TAIL DEPENDENCE OF OLS

This paper shows that if the errors in a multiple regression model are heavy-tailed, the ordinary least squares (OLS) estimators for the regression coefficients are tail-dependent. The tail dependence arises, because the OLS estimators are stochastic linear combinations of heavy-tailed random variables. Moreover, tail dependence also exists between the fitted sum of squares (FSS) and the residual sum of squares (RSS), because they are stochastic quadratic combinations of heavy-tailed random variables.

Non-Bayesian estimation of Weibull Lindley burr XII distribution

In this paper, we estimate the four parameters of Weibull Lindley burr distribution by using ordinary least square method and multiple regression least square method. The survival estimate made by using ordinary least square estimator (OLSE) and multiple regression estimator (MRE).

The Importance of Railroad Freight Transportation and Its Correlation to Economic Growth

In this study, the railroad freight transportation and economic growth relationship in Turkey was examined using the econometric analysis for the period of 1984-2017. As a result of the econometric analysis, it was found that there is no causal relationship between railroad freight transportation and economic growth in the long run, while there is a causality relationship from railroad freight transportation to economic growth in the short run. In addition to causality tests, an econometric model was created using stationary series, and the OLS method was applied for this model. According to this estimation result, an increase in railroad freight transportation increases economic growth. When this result obtained from the OLS estimation and the findings obtained from the Granger causality test are evaluated altogether, it can be stated that an increase in railroad freight transportation accelerates economic growth in the short run in Turkey.

Response Surface Model with Comparison of OLS Estimation and MM Estimation

Response Surface Method (RSM) is a collection of statistical techniques in the form of experiments and regression, as well as mathematics that is useful for developing, improving, and optimizing processes. In general, the determination of models in RSM is estimated by linear regression with Ordinary Least Square (OLS) estimation. However, OLS estimation is very weak in the presence of data identified as outliers, so in determining the RSM model a strong and resistant estimation is needed namely robust regression. One estimation method in robust regression is the Method of Moment (MM) estimation. This study aims to compare the OLS estimation and MM estimation method to get the optimal point of response in this case study. Comparison of the best estimation models using the parameters MSE and R^2 adj. The results of MM estimation give better results to the optimal response results in this case study.

A Test for Treatment Effects Based on the Exact Distribution of an Ordinary Least-Square Estimator in Sequential Parallel Comparison Design

Sequential parallel comparison design (SPCD) is used to deal with high placebo response when evaluating treatment effects in clinical trials. Several analysis methods assuming a continuous variable have been proposed within the SPCD framework. In particular, statistical tests using an ordinal least-square (OLS) estimator of an overall treatment effect have been discussed. These tests are usually adequate because the test statistic is asymptotically distributed as a normal distribution from the central limit theorem. However, the exact distribution of the OLS estimator has not been derived and the comprehensive performance of the test statistics is unknown. In this work, we derived the exact distribution of the OLS estimator and constructed a test based on the derived distribution within the SPCD framework. The performances (Type I error rate and power) of the test were compared with those of tests based on an asymptotic distribution (asymptotic tests). The Type I error rate of the exact test was well controlled, whereas that of the asymptotic method tended to deviate from the significance level. The results show that the power of the exact test is almost equivalent to that of the asymptotic tests.

A deep learning-based method for screening soil total nitrogen characteristic wavelengths

Spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. It would be convenient and practical for developing a low-cost portable detector of STN content based on spectroscopy if limited wavelengths could be singled out as soil nitrogen characteristic wavelengths. This paper proposed a deep learning-based method for STN characteristic wavelength screening by using a public dataset called LUCAS Soil. First, a multi-channel convolutional network incorporated Inception model was selected as the benchmark model for STN prediction because of its better performance than other models. Then, the absorbance of each wavelength of the full spectra (4200 wavelengths) was sequentially all set to zeros, and 4200 new spectra datasets were formed accordingly. Each new dataset still consisted of 4200 wavelengths, but the absorbance at one of these wavelengths was all set to zeros. The benchmark model was used to carry out STN predictions through each new dataset. In order to assess the effectiveness of STN prediction, an evaluation standard was established. Finally, according to the evaluation scores, the top highest 8–50 STN characteristic wavelengths were tentatively extracted from 4200 wavelengths (400–2499.5 nm). In the process of determining the benchmark model, three other predictive methods were carefully investigated besides the above deep learning-based model, which included ordinary least square estimation (OLSE), random forest (RF) and extreme learning machine (ELM). The results indicated that the deep learning-based model for STN prediction worked the best on large-scale and high-dimensional spectroscopic data (coefficients of determination (R2) = 0.93, root mean square error of prediction (RMSEP) = 0.97 g/kg, and residual prediction deviation (RPD) = 3.85), and accordingly it was chosen as the benchmark model. In addition, in order to verify the effectiveness of selected feature wavelengths, STN prediction models were established by using the top 8 to 50 of the STN characteristic wavelengths based on the above four different predictive methods. It was found that the ELM model with 21 characteristic wavelengths (R2 = 0.85, RMSEP = 2.05 g/kg, and RPD = 1.83) performed better than that with full-wavebands (R2 = 0.82, RMSEP = 2.49 g/kg, and RPD = 1.50). The accuracies of other models based on characteristic wavelengths were somewhat lower than that of the full-spectrum model, but it still was practical (R2 >= 0.57), which showed that the soil nitrogen characteristic wavelength screening method proposed in this paper was indeed effective, and the STN prediction model with a limited number of wavelengths could be established based on these STN characteristic wavelengths.

Urban/rural disparities in the wage effect of additional vocational education after formal education: the case of the Philippines

ABSTRACT The aim of this study is to investigate and compare rural and urban returns to additional vocational education after completing formal education in the Philippines. We estimate the returns to additional vocational education after formal education in formal employment and also rural/urban disparities for each industry sector. The investigation of the wage effect is based on the Mincerian Earnings Function, initially using OLS estimations and then correcting for sample selection bias using the Heckman method. Preliminary findings confirm that the wage effect is associated with additional vocational education for those completing secondary education, and the effect is more pronounced in rural than in urban areas. Thus, our principal finding is that vocational education is potentially more useful in rural areas, especially for those completing secondary education in the Philippines and especially considering that only about 6% of the workforce has completed any vocational education.

The impact of fintech on the profitability of state-owned commercial banks in China

Fintech is the innovative use of technology in the design and delivery of financial products and services. This study aims to statistically investigate the fintech and other determinants that affect the profitability of six state-owned commercial banks (SOBs) in China using the data from the 2014-2019 period. Theoretically, fintech serves as a disruption for its banking services through adverse impacts on asset business, liquidity business and intermediary business. Empirically, the result shows that the development of fintech has a negative impact on the profitability of SOBs and the regression results from the OLS estimation, the FGLS estimation, the Prais-Winsten estimation and the FE estimation are compared to ensure the validity of the statistical inference.

The economic and institutional determinants of trade expansion in Bronze Age Greater Mesopotamia

The focus of archaeologists on reconstructing exchange and communication networks in the past resulted in the enormous improvement of methods for analyzing material flows and detecting trade routes. However, our understanding of the determinants of trade patterns over time and space is still limited. To help tackle this issue, we study through regression analysis the rich economic and institutional experience of Bronze Age Greater Mesopotamia. Our testable predictions originate from three main economic theories of trade expansion. First, because of trade costs, mutually beneficial exchanges are discouraged by distance and encouraged by the relative size of markets. Second, trade expands when more suitable farming conditions in neighboring polities allow consumption risk-sharing. Finally, trade develops when interlocking exchange circuits ease the canalization of goods from the outside by providing secure routes, a more certain resolution of legal disputes and credit provision. Ordinary Least Squares—OLS—estimates based on data on 44 major Mesopotamian polities observed for each half-century between 3050 and 1750 BCE are consistent with these predictions. Our approach provides a robust theory-based empirical strategy for integrating archaeological, environmental, and historical data and calls for a tighter interdisciplinary cooperation.

Changes in Food Expenditures in Urban and Rural Egypt

The aim of this research is to trace recent changes in family expenditures on food commodities between 2015 and 2017/18. The data is drawn from CAPMAS publications on family income, expenditure, and consumption for both years. Three food groups are explored in this paper: 1- The aggregate group of food and beverages; 2-The grains and bread group; and 3- The meat group. Two statistical models are utilized for the purposes of data analysis. The first model is known as Working's model and the second model is the well-Known double logarithmic model. The analysis shows that the expenditure elasticity of the aggregate group of food and beverages ranges between 0.423 for high-income urban families in 2017/18 to 0.763 for urban poor families in 2015. For rural families the same elasticity is about 0.313 for high-income families in 2017/18 and about 0.751 for poor families in 2015. These estimates are derived from the OLS estimates of Working's model. The double logarithmic model gives one single estimate for expenditure elasticity for the entire set of data. For example, the expenditure elasticity for all families in 2017/18 is 0.63 in urban areas and 0.59 in rural areas according to the double logarithmic model. The same estimates are 0.64 for urban areas and 0.56 for rural areas according to Working's model. The Working's model fits the family survey data quite well and gives multiple estimates of expenditure elasticity according to the level of family income. In contrast, the double logarithmic model gives one estimate of expenditure elasticity that does not change with the level of family income. That is Working's model would be more suitable for policy analysis purposes.

Do Economic Policy Uncertainty and Geopolitical Risk Lead to Environmental Degradation? Evidence from Emerging Economies

Since the turn of twenty first century, economic policy uncertainty (EPU) and geopolitical risk (GPR) have escalated across the globe. These two factors have both economic and environmental impacts. However, there exists dearth of literature that expounds the impact of EPU and GPR on environmental degradation. This study, therefore, probes the impact of EPU and GPR on ecological footprint (proxy for environmental degradation) in selected emerging economies. Cross-sectional dependence test, slope heterogeneity test, Westerlund co-integration test, fully modified least ordinary least square estimator, dynamic OLS estimator, and augmented mean group estimator are employed to conduct the robust analyses. The findings reveal that EPU and non-renewable energy consumption escalate ecological footprint, whereas GPR and renewable energy plunge ecological footprint. In addition, findings from the causality test reveal both uni-directional and bi-directional causality between a few variables. Based on the findings, we deduce several policy implications to accomplish the sustainable development goals in emerging economies.

SOCIO-ECONOMIC AND INSTITUTIONAL DETERMINANTS OF FOREIGN DIRECT INVESTMENT IN SOUTH ASIA

Determinants of foreign direct investment (FDI) is the key area of research in development economics. Earlier studies did not give due weight to the segregation of analysis across economic, social, and institutional factors for FDI. This study specifically attempts to explore the intervening role of social and institutional factors which affects the relationship between economic factors and inward FDI. For this purpose, three segregate models relating to economic, social, and institutional factors along with a composite model comprised of all these factors are taken into consideration in this study. Panel data utilized is related to South Asian countries from 2008 to 2016. In the light of Breusch-Pagan LM Statistics, Pooled OLS estimates are preferred for analyses. This study leads to the conclusion that in segregation analysis economic factors and institutional factors have an important role in attracting FDI, but social factors have no significant role in this regard. The intervening role of social and institutional factors is not found in this study. The only factors which are recognized in the composite analysis are economic. Enlarged market size having an efficient financial market with no inflationary pressure is found to be an economic environment in the South Asian region which is helpful for attracting FDI. The rule of law is not found to be an effective characteristic for inward FDI.

Analysis and investigation of the conservativeness condition in the problem of parametric identification of distributed dynamic processes

The problem of bias of OLS estimates arises when solving the problem of parametric identification of distributed dynamic processes. There are various possible solutions to this problem. If the time series is trend-stationary, then these may be “ostationation” methods, which are generally difficult to apply. It is possible to use dimensionality reduction methods, but in this case we will still get biased estimates. In our previous works, it was shown that the problem of biased estimates can be solved using the conservativeness condition. The aim of this work was to investigate the possibility of using the conservativeness condition to improve the quality of estimates of the parametric identification problem, as well as to compare these results with the solution of the problem, in the case of applying a filter to it, as well as ridge regression.

Board Composition, Insider Ownership and Firm Performance: Evidence from Post-Shock Period of Stock Prices in Bangladesh

Purpose: This study attempts to analyze the influence of board structure (board size, board independence, CEO duality, insider ownership) on performance of Bangladeshi listed nonfinancial firms during the post-shock period of stock prices. Methodology: Putting stress on the issue of controlling for any possible endogeneity problems prevails in the effects of structure of board on corporate performance this study employed the Durbin-Wu-Hausman test as the presence of endogeneity severely makes the OLS estimates biased. Therefore, satisfying endogeneity and overidentification tests the observed data have been analyzed by 2SLS as well as OLS regression models. In an attempt to choose the consistent coefficients between OLS and 2SLS a Hausman test was applied. Findings: Results of the study suggest that a board featuring more directors improve accounting measures of performance and a board having more independent directors also significantly assist firms to enhance their market measure of performance. CEO duality is found detrimental to the accounting measures of performance. Apart from, findings support that holding more ownership by insiders leads to enhance firm performance. Thus, it provides empirical evidence to support a view of agency theory that large board, board independence, and insider ownership are good incentives to the firm to monitor and supervise managerial activities and minimize agency problems. Practical Implication: Overall, findings of the study are posing some implications for academics, practitioners, and policy makers in advancing the existing knowledge domain and formulating governance policies in the context of emerging countries like Bangladesh. Originality: The driving force of this work was to investigate how effective the corporate governance directives/notifications in regard to board independence and insider ownership was in protecting the shareholders’ interest during the post-shock period of stock prices in Bangladesh. Therefore, this study provides a robust result with regard to the impacts of reformed corporate governance notification (CGN 2012) on firm performance in Bangladesh.

The Impact of Mother’s Bargaining Power on the Nutritional Status of Children in Indonesia

Poor nutritional status, especially during childhood, has a negative impact on one’s early life as well as throughout their life. One of the factors that influences the improvement of children’s nutritional status is the bargaining power of the mother. Previous studies have limitations in that they often use cross-sectional data and indirect approaches to measuring bargaining power. This study aims to measure the impact of maternal bargaining power on children’s nutritional status in Indonesia. The unit of analysis is children aged 7–19 years (IFLS5) who still have and live with their parents (IFLS4). Using the OLS estimation method, the results show that maternal bargaining power significantly and positively influences the nutritional status of children (HA z-score).