scholarly journals Perspectives of Decentralized Powerloom Industry in India- An Empirical Analysis

2017 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Basavraj S Kudachimath ◽  
Shashidhar S Mahantshetti
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Human Resource ◽  
Empirical Analysis ◽  
Export Promotion ◽  
Positive Relation ◽  
Upward Trend ◽  
The World ◽  
Employment Generation

<div><p><em>Decentralized powerloom sector in Indi has always occupied a prominent place in the economic spheres of India. Present study pertains to the decentralized powerloom sector in India and its various dimensions. The data obtained from various reliable sources such as ministry of textiles, fibre2fashion, powerloom development and export promotion council and RBI reports were subjected to time series analysis and regression analysis. The results indicated there is an upward trend towards growth in terms of employment generation and production. The results pointed towards the decentralized sectors’ enormous potential to generate employment to both skilled and unskilled human resource in the country. The regression analysis showed a positive relation between the sector and GDP. In the light of the potentiality of the sector, suggestions have been put forth for harnessing the potential of the sector and make aid the country to be the most preferred source for clothing needs of the world.</em></p></div>

scholarly journals Road Traffic Offences in Nigeria: An Empirical Analysis using Buys-Ballot Approach

2021 ◽  
pp. 68-78
Author(s):  
K. C. N. Dozie ◽  
C.C Ibebuogu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Road Traffic ◽  
Study Data ◽  
Suitable Model ◽  
Upward Trend ◽  
Trend Pattern ◽  
Standard Deviations ◽  
Seasonal Indices

Road traffic offences in time series analysis when trend-cycle component is quadratic is discussed in this study. The study is to investigate the variance stability, trend pattern, seasonal indices and suitable model for decomposition of study data. The study shows that, the series is seasonal with evidence of upward trend or downward trend. There is an upsurge of the series in the months of March, August and November and a drop in January, June and December. The periodic standard deviations are stable while the seasonal standard deviations differ, suggesting that the series requires transformation to make the seasonal indices additive.

scholarly journals Did the UN convention on the rights of the child reduce child mortality around the world? An interrupted time series analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Christopher A. Tait ◽  
Abtin Parnia ◽  
Nishan Zewge-Abubaker ◽  
Wendy H. Wong ◽  
Heather Smith-Cannoy ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Child Mortality ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Rights Of The Child ◽  
Reduce Child Mortality ◽  
The World

scholarly journals Impact of campaign-style delivery of routine vaccines during Intensified Mission Indradhanush in India: a controlled interrupted time-series analysis

2020 ◽  
Author(s):  
Emma Clarke-Deelder ◽  
Christian Suharlim ◽  
Susmita Chatterjee ◽  
Logan Brenzel ◽  
Arindam Ray ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Immunization Coverage ◽  
Interrupted Time Series ◽  
Childhood Vaccination ◽  
World Health ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The World ◽  
The Impact

AbstractIntroductionThe world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization’s Global Vaccine Action Plan. In India, only 62% of children had received a full course of basic vaccines in 2016. We evaluated the Intensified Mission Indradhanush (IMI), a campaign-style intervention to increase routine immunization coverage and equity in India, implemented in 2017-2018.MethodsWe conducted a comparative interrupted time-series analysis using monthly district-level data on vaccine doses delivered, comparing districts participating and not participating in IMI. We estimated the impact of IMI on coverage and under-coverage (defined as the proportion of children who were unvaccinated) during the four-month implementation period and in subsequent months.FindingsDuring implementation, IMI increased delivery of thirteen infant vaccines by between 1.6% (95% CI: −6.4, 10.2%) and 13.8% (3.0%, 25.7%). We did not find evidence of a sustained effect during the 8 months after implementation ended. Over the 12 months from the beginning of implementation, IMI reduced under-coverage of childhood vaccination by between 3.9% (−6.9%, 13.7%) and 35.7% (−7.5%, 77.4%). The largest estimated effects were for the first doses of vaccines against diptheria-tetanus-pertussis and polio.InterpretationIMI had a substantial impact on infant immunization delivery during implementation, but this effect waned after implementation ended. Our findings suggest that campaign-style interventions can increase routine infant immunization coverage and reach formerly unreached children in the shorter term, but other approaches may be needed for sustained coverage improvements.FundingBill & Melinda Gates Foundation.

Use of the World Health Organization Access, Watch, and Reserve Classification to Follow Trends in Prescription of Antibiotic Use in Two Pediatric Intensive Care Units in Rio de Janeiro, Brazil

2020 ◽  
Vol 15 (03) ◽  
pp. 155-160
Author(s):  
André Ricardo Araujo da Silva ◽  
Cristina Vieira de Souza Oliveira ◽  
Cristiane Henriques Teixeira ◽  
Izabel Alves Leal
Keyword(s):  
Time Series ◽  
Intensive Care ◽  
Time Series Analysis ◽  
World Health Organization ◽  
Pediatric Intensive Care ◽  
Antibiotic Use ◽  
Antibiotic Consumption ◽  
World Health ◽  
The World ◽  
Health Organization

Abstract Objective The recommended percentage of antibiotic use in pediatric intensive care units (PICUs) using the World Health Organization (WHO) Access, Watch, and Reserve (AWaRE) classification is not known. Methods We have conducted an interrupted time series analysis in two PICUs in Rio de Janeiro, Brazil, over a period of 18 months. The type of antibiotics used was evaluated using the WHO AWaRE classification, and the amount of antibiotic was measured using days of therapy/1,000 patient-days (DOT/1000PD) after implementation of an antimicrobial stewardship program (ASP). The first and last semesters were compared using medians and the Mann–Whitney's test. The trends of antibiotic consumption were performed using time series analysis in three consecutive 6-month periods. Results A total of 2,205 patients were admitted, accounting for 12,490 patient-days. In PICU 1, overall antibiotic consumption (in DOT/1000PD) was 1,322 in the first 6 months of analysis and 1,264.5 in the last 6 months (p = 0.81). In PICU 2, the consumption for the same period was 1,638.5 and 1,344.5, respectively (p = 0.031). In PICU 1, the antibiotics classified in the AWaRE groups were used 33.2, 57.9, and 8.4% of the time, respectively. The remaining 0.5% of antibiotics used were not classified in any of these groups. In PICU 2, the AWaRE groups corresponded to 30.2, 60.5, and 9.3% of all antibiotics used, respectively. There was no use of unclassified antibiotics in this unit. The use of all three groups of WHO AWaRE antibiotics was similar in the first and the last semesters, with the exception of Reserve group in PICU 2 (183.5 × 92, p = 0.031). Conclusion A significant reduction of overall antibiotic use and also in the Reserve group was achieved in one of the PICU units studied. The antibiotics classified in the Watch group were the most used in both units, representing ∼60% of all the antibiotics consumed.

scholarly journals Oil, Nontax Revenue, and the Redistributional Foundations of Regime Stability

2009 ◽  
Vol 63 (1) ◽  
pp. 107-138 ◽  
Author(s):  
Kevin M. Morrison
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Foreign Aid ◽  
Social Spending ◽  
Government Revenue ◽  
Cross Sectional ◽  
Oil Companies ◽  
Regime Stability ◽  
The World ◽  
Regime Types

AbstractNontax revenues make up a substantial amount of government revenue around the world, though scholars usually focus on individual sources of such revenue (for example, foreign aid and state-owned oil companies). Using a theory of regime change that builds on recent models of the redistributional foundations of dictatorships and democracies, I generate hypotheses regarding all nontax revenue and regime stability. I argue that an increase in nontax revenue should be associated with less taxation of elites in democracies, more social spending in dictatorships, and more stability for both regime types. I find support for all three of these hypotheses in a cross-sectional time-series analysis, covering all countries and years for which the necessary data are available. Significantly, I show that the particular source of nontax revenue does not make a difference: they all act similarly with regard to regime stability and the causal mechanisms.

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

Employee turnover forecasting for human resource management based on time series analysis

2016 ◽  
Vol 44 (8) ◽  
pp. 1421-1440 ◽  
Author(s):  
Xiaojuan Zhu ◽  
William Seaver ◽  
Rapinder Sawhney ◽  
Shuguang Ji ◽  
Bruce Holt ◽  
...  
Keyword(s):  
Time Series ◽  
Resource Management ◽  
Time Series Analysis ◽  
Human Resource Management ◽  
Human Resource ◽  
Employee Turnover ◽  
Series Analysis

scholarly journals THE COMPARISON OF TWO PRECIPITATION PREDICTION METHODS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1025-1032
Author(s):  
X. Q. Mo ◽  
G. W. Lan ◽  
Y. L. Du ◽  
Z. X. Chen
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Daily Precipitation ◽  
Linear Regression Analysis ◽  
Time Series Model ◽  
Residual Error ◽  
Actual Situation ◽  
Monthly Average

Abstract. Precipitation forecasts play the role in flood control and drought relief. At present, the time series analysis and the linear regression analysis are two of most commonly used methods. The time series analysis is relatively simple as it only requires historical precipitation data. The model of the linear regression analysis can ensure high accuracy for causality analysis and short, medium and long-term prediction. Guilin is the region of the heavy rain center in Guangxi, which frequently suffers serious losses from rainstorms. Selecting a better model to predict precipitation has the important reference significance for improving the accuracy of precipitation weather forecast. In this research, the two methods are used to predict precipitation in Guilin. According to data of the monthly maximum precipitation, monthly average daily precipitation and monthly total precipitation from 2014 to 2016, this paper establishes the time series model and linear regression analysis model to predict precipitation in 2017 and compare the forecast results. The results show that the monthly average daily precipitation model is best with the accuracy of the time series model, and the residual error of predicted precipitation is 3.08 mm, but the change trend of predicted precipitation is not accord with the actual situation. The residual error is only 0.45 mm through using inter-annual linear regression equation to predict the precipitation, but the predicted summer precipitation is quite different from the actual one. The linear equation established by different seasons is used to predict the precipitation with residual error of 3.25 mm, and it is coincident for the predicted precipitation trend with the actual situation. Furthermore, the predictions fitting errors of spring, summer, autumn and winter are all less than 20%, which are within the scope of the specification prediction error.

scholarly journals The Empirical Analysis to Enhance Taiwanese Satisfaction in Buying Vietnam coffee

2017 ◽  
Vol 6 (3) ◽  
pp. 274
Author(s):  
Thi Nham Le ◽  
Chia Nan Wang ◽  
Ying Fang Huang
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Regression Analysis ◽  
Customer Satisfaction ◽  
Reliability Analysis ◽  
Empirical Analysis ◽  
Exploratory Factor Analysis ◽  
Integrated Approach ◽  
The World ◽  
The Empirical Analysis

<span lang="EN-US">Vietnam coffee industry has been well-known over the world for many decades. However, Vietnam products do not meet Taiwan customers’ expectation, it has lead to urgent challenges for the industry. Therefore, the paper proposed the integrated approach by using exploratory factor analysis, reliability analysis and regression analysis. The results of this study were used to <a name="OLE_LINK175"></a><a name="OLE_LINK174"></a>formulate and recommend on how to improve the products of Vietnam coffee by using SPSS statistics for analysis. The major findings of this paper was found out that there are six important determinants of Taiwanese decision-making in buying coffee. In order to enhance customer satisfaction with the coffee products from Vietnam, the companies need right strategies to improve these six groups of factors. The paper contributes meaningful and helpful results to the development of Vietnam coffee industry.</span>

scholarly journals Estimationof a Tax Compliance Index with Specific Application to Pay-As-You-Earn

2021 ◽  
Vol 04 (02) ◽  
Author(s):  
Cathrine T Koloane ◽  
Keyword(s):  
Time Series ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Composite Index ◽  
Tax Compliance ◽  
Upward Trend ◽  
Monthly Data ◽  
The World ◽  
The Future ◽  
Components Analysis

This article provides a composite index for Pay-As-You-Earn (PAYE)tax using Principal Components Analysis (PCA). The study uses time series from April 2012 to March 2020 (using monthly data) for the ratios derived from the four compliancemeasures namely, payments on time, registration on time, filing on time and accurate declarations. The index is computed using the weights of the four derived principal components. According to the model results, the PAYE tax compliance index averages around 75.0% for the period, with the lowest value of 72.3% in 2013/14 and the highestvalue of 77.1% achieved in 2018/19. There is a clear upward trend, indicating improving levels of compliance in PAYE. Similarly, setting the baseline index of 100 i.e. assuming 100% compliance for 2012/13, results in PAYE tax compliance index averaging around 101.6% for the period, with the lowest value of 97.72% in 2013/14 and the highest value of 104.26% achieved in 2018/19. The study recommends this methodology to be applied to all the tax products and that the overall tax compliance index be computed. This will assist tax authorities all over the world to actively monitor tax compliance levels and institute timeous corrective measures in order to address non-compliance and ultimately maximise PAYE revenue collections. Moreover, this study also serve as a base for many of the future tax compliance indices studies.

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