Unrelated Regression Research Articles

Ratio estimators for aboveground biomass and its parts in subtropical forests of Brazil

Researchers on forest biomass seek to develop or find methods of simple application, whose estimators reveals the best performance and provide the additivity of the biomass parts. Tree biomass normally is obtained by scaling tree parts (stem, branches, and leaves) separately. When the biomass is modeled by parts of the tree, their sum does not match the total, which means lack of additivity in the tree biomass estimation. We aimed to test and propose a modeling technique for estimating total aboveground biomass, so that consistency between tree parts will be achieved. The techniques ‘ratio estimators’ and ‘weighted apparently unrelated nonlinear regressions (WNSUR)’ were tested using a biomass dataset with 387 trees. Data were collected in eight sites located in Paraná and Rio Grande do Sul, Brazil, where information was collected on diameter at 1.30 m aboveground (dbh), heights (total, stem and crown), total biomass aboveground and its parts: stem, branches and leaves. All trees were identified at species level. To adjust the ratio equations, the cylinder volume (vcl), based on the mean square diameter (dqi2), was used as the independent variable and the total aboveground biomass and the respective parts: stem, branches, and leaves, as dependent variables. The occurrence of heteroscedasticity in the ratio estimators indicated that the data should be stratified into two stages, the first by dbh and the second by the slope of the ratio between the total aboveground biomass and the vcl of each tree. To group the trees in the second stage, a posteriori, a discriminant analysis was applied. The standard errors of the estimate resulted in less than 4% for the ratio estimators, while for the WNSUR they reached values larger than 40% for the total biomass aboveground and its parts. The statistics: bias, MAE, MSE and RMSE showed better performance by ratio estimators when compared to the WNSUR, for the total biomass and its parts except for the bias towards the stem. Ratio estimators naturally provide additivity to biomass parts making it possible to improve the precisions of the estimates. In future work with forest biomass and its parts, more canopy variables should be collected to improve the stratification of trees within the diameter stratum. The total volume of the trees should also be estimated, if possible, via xylometry, to approximate the ratio coefficients of the total aboveground biomass, as much as possible, to the actual values of the specific gravity of the wood.

The effect of agricultural subsidies on chemical fertilizer use: Evidence from a new policy in China

This study uses a nationally representative rural household survey from China, the difference-in-difference, three-step approach, and Seemingly Unrelated Regression methods to assess the impacts of China's new agricultural subsidy on chemical fertilizer use, heterogeneity effect, and mechanism. The results show that, first, the new agriculture subsidy reduces the use of chemical fertilizer by about 7.2 percent. A series of robustness tests confirm the finding. Second, the heterogeneity analysis shows that the subsidy's negative impact on fertilizer use is substantially greater among younger farmers than among older farmers. The negative effect also is significantly more in the main grain-producing areas than in non-grain-producing regions in China. Third, the mediating effect analysis shows that farmland scale mediates 8.3 percent of fertilizer use, and the adoption of agricultural machinery mediates 48.6 percent of fertilizer use. Our findings underscore the positive role that reforming the agrarian subsidy policy plays in sustainable development.

Farm consolidation and turnover dynamics linked to increased crop diversity and higher agricultural input use

ContextCrop-type composition and configuration at field and landscape scales are known to impact ecological functions in agricultural landscapes. However, links between regional farmland structures (e.g., variable farm size, ownership, configuration, etc.), which are important determinants of farm-level management decisions, and subsequent changes in agroecosystem impacts, remain understudied.Objective:To investigate these dynamics, we examined relationships between changing farm structures, as indicated by farm consolidation and persistence, and concurrent changes in crop diversity and agricultural resource use between 2002 and 2018 in Kern County, California.Methods:We integrated parcel-level datasets documenting farm size, operatorship, crop cover, pesticide usage and evapotranspiration rates for 2002 and 2018. We then applied seemingly unrelated regression analyses to quantify potential relationships between these key variables.Results and conclusions:In our study area we observed low (28%) persistence of farm operators from 2002 to 2018 and farm consolidation, as evidenced by a 43% increase in farm midpoint size in the studied time period. We further found significant associations between farm structures and agro-environmental outcomes. A doubling in farm size was associated with an 8% increase in crop diversity, but also with an 18% increase in pesticide-use and a 7% increase in water use at the farm level, raising concerns about the impact of large farms on landscape-scale resource use. Both crop diversity and water use were higher in persistent farms (8% and 11%, respectively) than in replaced or emergent farms. Our findings illustrate how regional shifts in farm size and operatorship may link to changes in crop diversity and resource use. SignificanceGiven continued concerns about the impacts that landscape simplification, pesticide use, and water shortages may have on agroecosystems both in our study region and beyond, our study highlights regional farm structure dynamics as a key element to investigate further and to consider in policies aiming to increase the sustainability of future agricultural landscapes.

The potential of bamboo forests as a carbon sink and allometric equations for estimating their aboveground biomass

Bamboo forests in Colombia and the Andean region of South America represent high-value ecosystems that provide ecological and economic benefits with local and global impacts. One of the ecosystem services provided by these forests is associated with their capacity to store carbon. In this study, data collected from monitoring plots were used to estimate the carbon content in different pools. Bamboo biomass (Bba), tree biomass (Btree), litter (Cli) and soil organic carbon (SOC) were assessed. The approximate total ecosystem carbon stock (TECaprox) ranged from 198.4 Mg C ha−1 to 330.9 Mg C ha−1 and bamboo carbon Cba represents approximately 50%. In addition, considering the relevance of developing tools to facilitate bamboo inventory and biomass estimates, allometric equations (AE) to estimate bamboo aboveground biomass (AGB) were fitted using the diameter of culms at breast height (dbh) and the total culm length (l) as predictor variables. The fitted AEs included the weighted linear, weighted log-transformed and weighted nonlinear fixed effect models. To compliance the additivity of biomass components a simultaneous systems of biomass equations (seemingly unrelated regressions) were also fitted. The precision and accuracy were assessed considering the residual diagnostic plots and statistics, such as the root-mean-square error (RMSE), RMSE percentage error (RMSEPE) and the Furnival’s index (Fln) for weighted log-transformed models and cross-validation. The performance of the models was similar with an RMSE of approximately 10 kg and 26% of RMSEPE, with slightly lower error for the weighted log-transformed model for the fitting and validation phases. A proper performance was also evidenced for the simultaneous approach for predicting AGB. Bamboo forests showed high relevance as carbon sinks and therefore might be considered strategic tropical ecosystems for climate change mitigation. On the other hand, the fitted AE exhibited proper performance and therefore provided reliable possibilities for estimating the AGB of bamboo during inventories. For practical reasons, the use of models with dbh as a predictor variable is recommended.

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Accurate large-scale biomass prediction is crucial for assessing forest carbon storage and dynamics. It can also inform sustainable forest management practices and climate change mitigation efforts. However, stand-level biomass models are still scarce worldwide. Our study aims to introduce the generalized additive model (GAM) as a convenient and efficient approach for forest biomass estimation. Data from 311 sample plots of three conifer species in northeastern China were used to evaluate the performance of the GAM model and compare it with traditional nonlinear seemingly unrelated regression (NSUR) models in predicting stand biomass, including total, aboveground, and component biomass. The results indicated that the goodness of fit of GAM was better than that of NSUR in two model systems. In the majority of cases, the scatter plots and prediction performance revealed that the stand total and component biomass models utilizing GAM outperformed those based on NSUR. Disregarding heteroscedasticity and requiring fewer statistical assumptions provide additional support for the replacement of NSUR-based models with GAM-based models. This study implies that the GAM approach has greater potential for developing a system of stand biomass models.

Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+

Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+

The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application

This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression RSUR and proposes the Kibria-Lukman KLSUR estimator for the parameters of the Seemingly Unrelated Regression (SUR) model when the regressors of the models are collinear. A simulation study was conducted to compare the performance of the three different types of estimators for the SUR model. Different correlation levels (0.0, 0.1, 0.2, ..., 0.9) among the independent variables, sample sizes replicated 10000 times and contemporaneous error correlation (0.0, 0.1, 0.2, ..., 0.9) among the equations were assumed for the simulation study. The efficiency of the three (RFGLSE, RSUR, and KLSUR estimators for SUR, when the predictors are correlated, was investigated using the Trace Mean Square Error (TMSE). The results showed that the KLSUR estimator outperformed the other estimators except for a few cases when the sample size is small.

Mathematical system based on taper functions for distribution by structural product of culms in three giant bamboo taxa

Aim of study: To generate a mathematical system to distribute structural products of bamboo culms.
 Study area: Northeastern region of the state of Puebla, Mexico.
 Materials and methods: Eighty-seven culms of Bambusa oldhamii Munro, Guadua aculeata Rupr. and Guadua angustifolia Kunth were collected in Puebla, Mexico. Four taper functions were evaluated, the one with the best predictive capacity was fitted to model the diameter over and under wall together with a wall thickness model. The fitting strategy consisted of a system of additive equations using Weighted-Nonlinear Seemingly Unrelated Regression (WNSUR) procedure with autocorrelation correction, in combination with the Dummy Variable technique.
 Main results: The Fang & Bailey case 1-a model was selected to describe the diameter over and under wall; the Cao and Papper model was used to model the wall thickness. The R2adj of the system fitted were 0.977, 0.944 and 0.918, and RMSE values 0.186 cm, 0.200 cm and 0.134 cm, for diameter over wall, diameter under wall, and wall thickness, respectively. G. angustifolia had the greatest taper and wall thickness, followed by G. aculeata. The highest proportion of primary product was presented by G. angustifolia. The system generated had parameters specific for each bamboo taxon.
 Research highlights: The diameter profile of bamboo culms can be modeled by taper functions. A mathematical system for distribution by structural product type was developed consisting of: (1) a taper model for the diameter over wall and, (2) a function to estimate the commercial height.

Revisiting the Impact of NPAs on Profitability, Liquidity and Solvency: Indian Banking System

In the light of the fact that non-performing assets (NPAs) in the country are increasing at a very fast pace, this article intends to investigate the outcome of gross NPAs (GNAPs) ratio on profitability, liquidity and solvency in Indian banking system. To study this impact, we have used panel data of 30 Indian banks (12 government sector banks and 18 private sector banks) from 2014 to 2021 (8 years) collected from Prowess (CMIE) and money control. The current study uses four different panel regression models, that is, fixed and panel regression models, pooled regression models and seemingly unrelated regression models. The empirical outcomes of the current study confirm the outcomes of existing studies. The findings of this article confirm the substantial association between the GNPA ratio and profitability ratio, that is, net profit ratio, return on assets ratio and return on equity ratio. Further, the study also confirms the association between the GNPA ratio and liquidity ratios of banks (cash flow margin, current ratio, acid test ratio, cash ratio and operating cash flow ratio). We also found the impact of the GNPA ratio on the capital adequacy ratio.

Fix effect sur to analyze economic growth in developed and developing countries

This study aims to identify the relationship between population density, inflation, and unemployment rates on the human development index, GNP, export-import, and urbanization in the developed and developing countries category using the Fix Effect Seemingly Unrelated Regression (FE SUR) with a dummy variable as the slope component. This research necessitates the development of the Seemingly Unrelated Regression model, specifically the Panel Seemingly Unrelated Regression (Panel SUR) model with a dummy variable as the slope component, due to the dynamic nature of the data and the fact that the same set of predictor variables explains the five response variables. The Panel, the Seemingly Unrelated Regression model with dummy variables, can accommodate research objectives where the SUR model can explain the influence between variables, differences in characteristics between countries can be explained by fixed effect models, and differences in the effect of population density, inflation, and unemployment rates on the human development index, GNP, exports imports and urbanization in the categories of developed and developing countries can be explained by slope dummy variables. The results showed that 98.46% of the diversity of response variables (human development index, GNP, exports, imports, and urbanization) could be explained by predictor variables (population density, inflation, and unemployment rate), while the other 1.54% was explained by other factors not included in the fixed effect SUR model. In addition, the results show that population density has a significant positive relationship with GNP, imports, and exports. However, there is a significant negative relationship between unemployment and GNP. There are large differences in the relationship between the unemployment rate and GNP in developed and developing countries, whereas in developed countries, there is a larger and negative relationship compared to developing countries.

Short supply chain choice and impact amongst rice processors in rural Ghana

PurposeThis paper examined the factors influencing the participation of rice processors in short supply chains and the participation impact on the amount of rice processed, per capita expenditure of household and value of sales.Design/methodology/approachThe Seemingly Unrelated Regression and Doubly Robust Augmented Inverse Probability Weighting Model (AIPW) were used to analyse the determinants of short supply chain participation and the impact of short supply.FindingsFrom the results, the mean value of rice processed was GH₵18385 (US$ 3,069.28), with the minimum value being GH₵ 25 (US$ 4.17) and the maximum GH₵ 67200 (US$ 1,1218.70) per annum. Processed rice aroma and grade characteristics positively influence the value of processed rice sold via short supply chains as well as the expertise rate of the processor, Farmer-Based Organisation membership, and marketing information availability. Women rice processors' per capita expenditure, total sales value and the value of processed rice was positively influenced by the short supply chain participation.Research limitations/implicationsEven though the sample size was appropriate, a larger sample size could further support the study's finding since a limited geographical area with predominant domestic rice processors was studied. Again, future studies should consider behavioural theories, such as the Theory of Planned Behaviour, amongst others, in understanding the reasons for the choices of short supply chains compared to other sales outlets.Originality/valueAlthough there is a growing body of literature on rice, most of the studies focussed on the marketing outlet of rice producers, rice processing, constraints and opportunities faced by rice farmers and processors and an out-grower scheme involving rice processors amongst rice producers with none of these on the choice of short supply chains amongst women processors. Also, amongst all the studies on rice producers, none applied a theory; however, the Women in Development (WID) Theory was used to analyse the impact of the short supply chain on the impact on household per capita expenditure (poverty), the value of sales and amount of rice processed, a modest theoretical contribution of the paper to literature.

Lines of Credit and Family Firms: The Case of an Emerging Market

Lines of credit constitute an integral part of a firm’s liquidity policy; however, there is limited research on lines of credit in emerging markets. This study fills this gap by examining firm incentives to access and draw from lines of credit using the context of Jordan, a bank-based emerging market, focusing on the impact of family firms. To account for the endogeneity of family control, this study estimates the probability of accessing a line of credit using seemingly unrelated bivariate probit regression and its usage using treatment effect regression. The article documents that family firms are less likely to obtain a line of credit and their drawdowns are smaller compared to non-family firms. These findings support the agency’s view on having and using lines of credit. Other findings are consistent with a substitution effect between internal and external liquidity sources which implies a cost wedge between the two sources of liquidity.

The cost-effectiveness of an indicated blended care intervention in primary care compared to usual care in patients with moderate persistent somatic symptoms

IntroductionAppropriate treatment for people with an increased risk for developing chronic Persistent Somatic Symptoms (PSS) is of great importance at an early stage to improve quality of life and prevent high costs for society. ObjectiveTo evaluate the cost-effectiveness of an integrated blended care intervention compared to usual care for QALYs, subjective symptom impact and physical and mental health status in patients with moderate PSS. MethodsThis economic evaluation was conducted alongside a 12-month prospective, multicenter cluster randomized controlled trial in Dutch primary care. 80 participants received the intervention and 80 participants received usual care. Seemingly unrelated regression analyzes were performed to estimate cost and effect differences. Missing data were imputed using multiple imputation. Bootstrapping techniques were used to estimate uncertainty. ResultsWe found no significant difference in total societal costs. Intervention, primary and secondary healthcare and absenteeism costs were higher for the intervention group. The ICER for QALYs demonstrated the intervention was on average less costly and less effective compared to usual care. For the subjective symptom impact and physical health, the ICER indicated that the intervention group was on average less costly and more effective. For mental health, the intervention was on average more costly and less effective. ConclusionWe didn't find an integrated blended primary care intervention to be cost-effective compared to usual care. However, when looking on relevant, but specific outcome measures (subjective symptom impact and physical health) for this population, average costs are found to be lower and the effectiveness found to be higher.

Impact of Financial Development and Economic Growth on Energy Consumption in Developing Countries of Asia

This study investigates the impact of financial development and economic growth on energy consumption by controlling variables such as urbanization and globalization in developing countries of Asia for the era of 1991-2019. Data related to financial development, economic growth, energy consumption and urbanization is collected from World Development Indicator and data related to globalization is collected from Konjunkturforschungsstelle (KOF) index of globalization. In this research Dynamic seemingly unrelated regression model is applied to test the hypothesis. According to the outcomes of DSUR the impact of financial development on energy consumption is positively substantial as increase of 1 unit in financial development brings 3.07% rise in energy consumption, the effect of GDP on energy consumption is positively influential as increase of 1 unit in GDP brings 0.29% increase in energy consumption and the influence of globalization is unfavorable but substantial as increase of 1 unit in globalization brings decrease of 15.57% in energy consumption. Moreover, the influence of urbanization on energy consumption is positive and considerable, as increase of 1 unit in urbanization brings 11.54% increase in energy consumption. Moreover, there is two way connections among GDP and financial development. Moreover, Asian countries should adopt energy conservation policies.

Electricity Consumption and Population Growth in South Africa: A Panel Approach

This study investigates the relationship between population growth and electricity consumption in South Africa for the period from 2002 to 2021 collected from StatsSA. The study utilises Seemingly Unrelated Regression model and Dumitrescu and Hurlin (2012) causality tests to analyse the relationship between the variables. Empirical results revealed that there is a negative statistically significant relationship between population growth and electricity consumption in South Africa. The results further reveal one-way causality running from population growth to electricity consumption. The study recommends that the government and policy makers must implement policies aimed at increasing renewable electricity generation to match the gap between electricity demand and growing population thereby reducing constant loadshedding in South Africa.

Multivariate mixed-effects models for stand characteristics of hybrid aspen plantations in southern Finland and southern Sweden

Hybrid aspen, a hybrid between the European aspen and North American trembling aspen (Populus tremula L. × P. tremuloides Michx.), is a promising species because of its fast-growth and its suitability for multi-purpose use. However, models for predicting the age-dependent development of stand characteristics are still missing. The main objectives of this study were therefore to develop the models for predicting stand characteristics of hybrid aspen plantations and to validate the model applicability. The target response variables were stand basal area (BA), basal area-weighted mean diameter (DG) and basal area-weighted mean height (HG). Data were obtained from clonal hybrid aspen trials in southern Finland and southern Sweden. Multivariate mixed-effects modelling was used to estimate the parameters of seemingly unrelated regression for BA, DG, and HG. Model fit provided the following predictor variables: stand age (AGE), the number of trees per hectare (TPH), site index (SI), growing degree-days (GDD5), soil and site type, and thinning treatment. The chosen predictors differed slightly by response variable, but all parameters were highly significant (P < 0.0001), and model goodness-of-fit statistics presented high accuracy: RMSE of 2.59 m2 ha−1 for BA, 1.21 cm for DG, 1.05 m for HG in arithmetic scale. The applied simulations illustrated clear differences in the predicted development of stand characteristics when input variables SI, TPH or GDD5 changed. The developed models were assessed to be easily applicable and useful for predicting the stand and tree characteristics of clonal hybrid aspen plantations, especially for the stands with AGE ≤ 30 years and TPH ≤ 2000 trees ha−1.

Farmland allocation in the conversion from conventional to organic farming

Organic farming enhances food quality and public health, and contributes to a more sustainable environment. Although certified organic farmland grew from 11 to 72.3 million hectares between 1999 and 2019, it constituted only 1.5 percent of the world’s agricultural farmland in 2019. The main impediment to the conversion from conventional to organic farming is the financial difficulties that farmers experience during the transition period in terms of decrease in yield and increase in farming costs owing to transitional practices. Furthermore, uncertainty in crop price and yield may aggravate the adverse effects of transitional practices. This article presents a multi-period optimization model for the allocation of farmland among crops and agricultural practices which allows farmers to plan a transition to organic farming while incurring a bounded shortfall of income. We calibrate our model to represent a grower of corn and soybean in Iowa and, using a seemingly unrelated regression model, crops revenues are simulated and utilized in the numerical experiments. The results show that i) our optimized crop rotation pattern outperforms other policies in the agriculture industry, including monoculture and systematic crop rotation, and that ii) our gradual conversion plan mitigates the chance of profit shortfalls.

The Oligopolistic Behavior of Kazakh and Russian Wheat Exporters in the South Caucasus: Evidence from a Residual Demand Elasticity Analysis

This study looks at whether Kazakh and Russian wheat exporters leverage their dominant share of the wheat markets in the South Caucasus to exercise market power. We apply a three-stage estimation for systems of simultaneous equations and Zellner’s seemingly unrelated regression to analyze residual demand elasticity. The results of both estimations provide empirical evidence of Russian market power in the wheat markets of the South Caucasus but no evidence of a Kazakh oligopoly. Russian exporters possess greater market power in Armenia than in Georgia. Market power depends on the presence of competitors in the destination market. The results show that Kazakh exporters restrict the market powers of Russian exporters in the Azerbaijani wheat market, while Russian exporters constrain the market power of Kazakh exporters in the Azerbaijani and Georgian wheat markets. Ukrainian wheat exporters are able to intervene in the market powers of Russian exporters in Azerbaijan and Georgia, while they restrict Kazakh oligopoly in the Georgian market. Some export restrictions imposed by wheat exporting countries significantly affected competition in wheat importing countries. Russia’s ongoing war against Ukraine and the blockade of Ukrainian ports on the Sea of Azov and the Black Sea are restricting wheat trade, severely damaging Ukraine’s competitiveness and export potential, and reducing food security in the South Caucasus, the Middle East, and North Africa.

Impact of Covid-19 on leisure consumption in the US

ABSTRACT The COVID-19 pandemic has changed the way time is allocated towards market and non-market activities. This paper looks at the effect of the pandemic on a key category of non-market activity – leisure. We use micro-data from the American Time Use Survey to see how the pandemic affected leisure in the United States. A seemingly unrelated regression system is deployed to examine how the pandemic affects various leisure activities such as television viewing, socialising, reading/writing, active sports, games, and computer use. Apart from socialising, which was actively discouraged during the pandemic, all major leisure activities saw an increase, which resulted in overall leisure going up in the Covid year 2020 by 5%, compared to 2019. However, the impact is statistically significantly different for different types of leisure activities. Active leisure is being consumed less while passive leisure is being consumed more – pointing towards an increased sedentarization of lifestyles. Since previous studies have shown passive leisure to be negatively associated with health, our result has important implications for the health and well-being of Americans, and if the trend continues, policymakers should actively incentivise the consumption of active forms of leisure.

Study of impacts of blockchain technology on renewable energy resource findings

Using daily data from the beginning of 2018 to the end of 2022 and the Seemingly Unrelated Regression method, this paper sought to determine the relationship between green and non-green cryptocurrency indices as representative of blockchain technologies on three green bond indices in two periods before the outbreak of COVID-19 and the corona period. The preliminary results confirmed that by spreading the coronavirus, the sensitivity of the green bond market has increased to the gold index. In addition, the green cryptocurrency index (Cardano) significantly impacts green bonds more during the Corona era. The negative effect of the Bitcoin index on the index of green bonds in the Corona era is less than its adverse effect in the pre-corona period. Developing green cryptocurrencies and linking cryptocurrencies and digital green financing are two significant recommended policy implications for scholars and policymakers.