scholarly journals An application of the spatial regression model for Vietnam’s export: province-level approach

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Nguyen Van Si ◽  
Nguyen Viet Bang
Keyword(s):  
Regression Model ◽  
Quantitative Methods ◽  
Export Performance ◽  
Spatial Regression ◽  
Secondary Data ◽  
Labor Costs ◽  
Factors Affecting ◽  
Qualitative And Quantitative Methods ◽  
Spatial Regression Model ◽  
New Research

The paper attempts to define and measure factors affecting export of provinces/ cities in Vietnam, using both qualitative and quantitative methods. In particular, the former was conducted through in-depth interviews with 15 experts (including 3 academics, 7 export officials of Departments of Industry and Trade and 5 managers of export enterprises) in December 2018. The latter was performed using spatial regression model on secondary data of 63 provinces/cities from 2013 to 2017. The paper provides a new research methodology on export of Vietnam’s provinces/ cities (i.e. the spatial regression model). The empirical results show that there is a positive correlation between neighboring provinces/ cities in export activities, indicating that the good export performance of one province boosts that of its neighbors. The results also show that the export of Vietnam’s provinces/ cities is affected by GDP, import value, investment capital, and labor costs.

Factors Affecting Loan Repayment Performance of Microfinance Institution Borrowers

2019 ◽  
Vol 8 (2) ◽  
pp. 27-43
Author(s):  
Akalewold Fedilu Mohammed ◽  
Mesfin Hirpato Wobe
Keyword(s):  
Random Sampling ◽  
Quantitative Methods ◽  
Secondary Data ◽  
Sampling Technique ◽  
Loan Repayment ◽  
Factors Affecting ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
Microfinance Institution ◽  
Major Factors

This study investigated the factors that affect the loan repayment performance of Omo Microfinance Institution borrowers at Wondo Genet Woreda, Ethiopia. Both primary and secondary data were used in the study. The required data were collected from 225 borrowers of Omo Microfinance. Respondents were selected by a stratified random sampling technique. Both qualitative and quantitative methods of analysis were used. The findings of the study revealed that 44.9% of borrowers in the study area did not repay the amount of money they borrowed as per credit schedules. The major factors that affect the loan repayment performance of borrowers were their sex, educational level, family size, borrowing experience, timelines of loan, repayment period and advisory visit.

scholarly journals Internal factors affecting vegetable and fruit export performance of Vietnamese firms

2019 ◽  
Vol 3 (2) ◽  
pp. 153-165
Author(s):  
Le Tan Buu ◽  
Pham Ngoc Y
Keyword(s):  
Chief Executive Officers ◽  
Quantitative Methods ◽  
Export Performance ◽  
Chief Executive ◽  
Internal Factors ◽  
Factors Affecting ◽  
Qualitative And Quantitative Methods ◽  
Executive Officers ◽  
Technology Orientation ◽  
Depth Interviews

The paper aims at defining and measuring key internal factors which impact on export performance of vegetable and fruit export firms in Ho Chi Minh City, Lam Dong, Dong Nai, Binh Duong, Vung Tau, Binh Phuoc, Tay Ninh, Long An và Tien Giang province. The study uses resource-based theory (RBV) to explain the internal factors affecting the export performance by applying qualitative and quantitative methods. The qualitative method is carried out through in-depth interviews of 10 chief executive officers, while the quantitative one is conducted through direct interviews with 228 managers of vegetable and fruit companies. Export performance are measured under the subjective perspective to collect information from firms, considering the perception or satisfaction of firms on export activities. The results show that firm's export performance is under the direct influences of four internal factors including: (1) International experience; (2) export commitment; (3) product characteristics; (4) technology orientation. The study measures export performance and internal factors affecting export performance, thereby suggesting management implications that help businesses improve export performance of Vietnamese fruit and vegetable firms.  

scholarly journals Examination of the apricot variety use and the factors affecting variety use in the Gönc production area

2010 ◽  
Vol 16 (3) ◽  
Author(s):  
D. D. Nagyné ◽  
J. Nyéki ◽  
M. Soltész ◽  
Z. Szabó
Keyword(s):  
Data Collection ◽  
Rural Areas ◽  
Quantitative Methods ◽  
Research Work ◽  
Value Added ◽  
Secondary Data ◽  
Relevant Information ◽  
Factors Affecting ◽  
Qualitative And Quantitative Methods ◽  
Empirical Survey

Hungary is a traditional fruit growing country for ages. As fruit sector has a very high hand work request and value added, it has an important role to decrease the elimination of unemployment and the lack of income in the disadvantage rural areas. The study was made in the year of 2009, the studied population consisted of the members of the fruit-grower marketing organization (Gyümölcsért Ltd.), that organizes growing and sales of stone fruits in Hungary. The studied area of this Ltd is in North Hungary. The growers, who filled the questionnaire, were selected random simple sample. Two data collection were used during our research work: primer and secondary data collection. The resources of the primer data-collection were the questionnaires of our empirical survey that have been completed by the relevant information from informal interviews with farmers (who previously filled the questionnaires in). We introduced and analysed the local (county level) and the wider (region level) farming conditions by the secondary data. By the composition of the questions both qualitative and quantitative methods have been used. This current study intends to represent one part of this comprehensive research.We wish to briefly introduce mainly the research results concerning variety use.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

The Influence of Climate Factors on Cocoa Productivity in Sulawesi, 2019

2021 ◽  
Vol 1 (1) ◽  
pp. 21-30
Author(s):  
Marta Sundari ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Regression Model ◽  
Economic Activity ◽  
Oil And Gas ◽  
Climatic Factors ◽  
Average Length ◽  
Spatial Regression ◽  
Model Estimation ◽  
Climate Factors ◽  
Spatial Regression Model ◽  
Average Monthly Rainfall

Cocoa is one of the plantation commodities that has an important role in Indonesia's economic activity and is one of Indonesia's export commodities which is quite important as a source of foreign exchange and oil and gas. Sulawesi Island is one of the cocoa-producing islands in Indonesia. This study aims to determine a spatial regression model between the average cocoa productivity per month with the average drinking temperature per month, the average monthly rainfall and the average length of sunshine per month and the climatic factors that affect cocoa productivity in Sulawesi. The best model estimation uses the AIC value; the best model has the smallest AIC value. In this study, the SARMA spatial regression model is the best model with the specified criteria.

scholarly journals Examining Multilevel Poverty-Causing Factors in Poor Villages: a Hierarchical Spatial Regression Model

2021 ◽  
Author(s):  
Yanhui Wang ◽  
Yuewen Jiang ◽  
Duoduo Yin ◽  
Chenxia Liang ◽  
Fuzhou Duan
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Spatial Regression ◽  
Hierarchical Linear Model ◽  
Central China ◽  
Spatial Effects ◽  
Spatial Regression Model ◽  
Village Level ◽  
The Village

AbstractThe examination of poverty-causing factors and their mechanisms of action in poverty-stricken villages is an important topic associated with poverty reduction issues. Although the individual or background effects of multilevel influencing factors have been considered in some previous studies, the spatial effects of these factors are rarely involved. By considering nested geographic and administrative features and integrating the detection of individual, background, and spatial effects, a bilevel hierarchical spatial linear model (HSLM) is established in this study to identify the multilevel significant factors that cause poverty in poor villages, as well as the mechanisms through which these factors contribute to poverty at both the village and county levels. An experimental test in the region of the Wuling Mountains in central China revealed the following findings. (1) There were significant background and spatial effects in the study area. Moreover, 48.28% of the overall difference in poverty incidence in poor villages resulted from individual effects at the village level. Additionally, 51.72% of the overall difference resulted from background effects at the county level. (2) Poverty-causing factors were observed at different levels, and these factors featured different action mechanisms. Village-level factors accounted for 14.29% of the overall difference in poverty incidence, and there were five significant village-level factors. (3) The hierarchical spatial regression model was found to be superior to the hierarchical linear model in terms of goodness of fit. This study offers technical support and policy guidance for village-level regional development.

scholarly journals Integrated Reporting in Southeast Asia: Does Value Creation Work?

2021 ◽  
Vol 10 (5) ◽  
pp. 57
Author(s):  
Arie Pratama ◽  
Winwin Yadiati ◽  
Nanny Dewi Tanzil ◽  
Jadi Suprijadi
Keyword(s):  
Firm Value ◽  
Structural Equation ◽  
Quantitative Methods ◽  
Role Model ◽  
Secondary Data ◽  
Equation Modeling ◽  
Integrated Reporting ◽  
Factors Affecting ◽  
Corporate Reporting

This study describes the factors affecting the quality of integrated reporting (IR) disclosure and how the disclosures affect firm value. This study employed quantitative methods with secondary data. This study sample includes 1,900 firms from 2016 to 2018. Descriptive statistics, cluster analysis, and structural equation modeling path analysis were used to describe the development. This study showed that the IR implementation in five countries currently has an adequate score. Hypothesis testing showed that three factors influenced the size of IR disclosures and the disclosures influence the firm value. This study implies that although IR in the current and future will be a role model for corporate reporting, Southeast Asian firms still need to strengthen the quality of IR. This study contributes to the current development and description of IR, which is limited because of its recent introduction, in five countries: Indonesia, Malaysia, Philippines, Singapore, and Thailand.   Received: 28 April 2021 / Accepted: 15 July 2021 / Published: 5 September 2021

scholarly journals PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

2018 ◽  
Vol 7 (4) ◽  
pp. 346
Author(s):  
NI MADE LASTI LISPANI ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Population Density ◽  
Regression Model ◽  
Average Length ◽  
Moving Average ◽  
Spatial Regression ◽  
Higher Order ◽  
Spatial Effect ◽  
Spatial Regression Model ◽  
Spatial Autoregressive ◽  
Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime  are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

scholarly journals A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3226-3241 ◽  
Author(s):  
CE Utazi ◽  
J Thorley ◽  
VA Alegana ◽  
MJ Ferrari ◽  
K Nilsen ◽  
...  
Keyword(s):  
High Resolution ◽  
Regression Model ◽  
Random Effects ◽  
Vaccination Coverage ◽  
Spatial Regression ◽  
Demographic And Health Surveys ◽  
Linear Predictor ◽  
Areal Data ◽  
Spatial Regression Model ◽  
Health And Development

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

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