scholarly journals USE OF DYNAMIC REGRESSION MODEL FOR REDUCTION OF SHORTAGES IN DRUG SUPPLY

2019 ◽  
Vol 17 (2) ◽  
pp. 218-231
Author(s):  
Aurelija Burinskienė
Keyword(s):  
Regression Model ◽  
Time Delays ◽  
Lead Time ◽  
Time Frame ◽  
Drug Supply ◽  
Drug Shortage ◽  
Chi Square ◽  
Lm Test ◽  
The Future ◽  
Dynamic Regression Model

The study is given to the use of dynamic regression model for reduction of shortages in drug supply: Purpose – the use of a dynamic regression model to identify the influence of lead-time on the reduction of time delays in drugs supply. To reach the goal, the author focuses on the improvement of drugs availability and the minimisation of time delays in drugs supply. Research methodology – the application of dynamic regression method to minimise shortage. The author suggests a dynamic regression model and accompanies it with autocorrelation and heteroskedasticity tests: Breush-Godfrey Serial Correlation LM Test for autocorrelation and ARCH test for heteroskedasticity. Findings – during analysis author identifies the relationship between lead-time and time delays in drugs supply. The author delivers a specific regression model to estimate the effect of deterministic lead-time on shortage. Probability F and Probability Chi-Square of this testing show that there is no significant autocorrelation and heteroskedasticity. Research limitations – the research is delivered for a one-month time frame. For the future, the study could review other periods. The author has incorporated the lead-time component in shortage reduction study by leaving capacity uncertainty component unresearched. The future studies could incorporate both elements into shortage reduction case analysis. Practical implications – presented framework could be useful for practitioners, which analyse drug shortage reduction cases. The revision of supply time table is recommended for pharmacies aiming to minimise the shortage level. Originality/Value – the analysis of deterministic lead-time and identification that the periodicity of shortage is evident each eight days. The study contributes to lead-time uncertainty studies where most of the authors analyse the stochastic lead-time impact on shortages.

scholarly journals An E-Commerce Economic Dynamic Data Evaluation Model Based on Multiuser Demand Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chenyuan Wang
Keyword(s):  
Regression Model ◽  
Evaluation Model ◽  
Empirical Studies ◽  
Evolutionary Game ◽  
Listed Companies ◽  
Financial Reports ◽  
Economic Dynamic ◽  
Future Earnings ◽  
The Future ◽  
Dynamic Regression Model

Forecasting the future earnings of listed companies based on multiuser constraints is the focus of investors, securities dealers, creditors, and management. Some empirical studies at home and abroad indicate that the financial reports issued by listed companies regularly contain information about future changes in earnings. On this basis, this article uses the Bayesian dynamic regression model to predict the changes in the future earnings of listed companies and compares the results with traditional analysis models. Through case analysis, it can be seen that the prediction effect of the Bayesian dynamic regression model is generally better than that of the traditional regression model. The Bayesian model can better predict the results, and through the prediction results, it can also establish an evolutionary game model of the industrial innovation replication dynamic system, which can assist enterprises in making profit decisions.

scholarly journals Volatility Analysis of Stock Returns for Fifteen Listed Banks in Chittagong Stock Exchange

2018 ◽  
Vol 11 (9) ◽  
pp. 129 ◽  
Author(s):  
MD Rokonuzzaman ◽  
Mohammad Akram Hossen
Keyword(s):  
Regression Model ◽  
Stock Returns ◽  
Stock Exchange ◽  
Statistical Tests ◽  
Dynamic Panel Data ◽  
The Other ◽  
Series Data ◽  
Arch Effect ◽  
Lm Test ◽  
Dynamic Regression Model

The aim of the study is to analyze and prediction of return for 15 popular banks in Chittagong Stock Exchange. The economic development of a country depends largely on the effective performance of stock market. In this study, secondary data from the CSE, Bangladesh with a sample period 1st January 2009 to 27th December 2015 for selected 15 banks, listed in Chittagong Stock Exchange. Descriptive statistics, important graphs, statistical tests, fitted dynamic regression models with ARCH effect are used to complete the analysis. It is found that for all banks, the return occurs high with a high risk and risk is low for the companies with small amount of return. The daily log returns for all companies are almost normally distributed. Checking the stationarity of the log returns data getting from all banks in both graphical and statistical unit root method, time series data are found to be stationary. In the dynamic regression model the log return Yt is considered as dependent variable and the log daily average Xt is considered as independent variable. The average VIF for the returns of all banks are found less than 10, indicate not severity of multicollinearity and ∆Yt , ∆2Yt , ∆Xt , ∆2Xt can be used as the explanatory variables in the model where ∆ indicates the difference operator. Lagrange multiplier (LM) test based on the residuals of the regression model is significant for all the banks implies that the data have the conditional heteroscadisticity in the behavior of their residuals. The line diagrams conferred the complete randomness in Parkinson’s monthly volatility for every company. The log return of six out of 15 banks have significant ARCH effect with 2 period lags and rest of the banks, the log returns have significant ARCH effect with 1 period lag. The regression coefficients of and have the negative effects on and the other coefficients have both positive and negative effect. A modified ARDL (2,2) model is proposed and 1-step ahead forecasted model for different banks are recommended.One can try to estimate the confidence interval for the parameters used in modified model in his/her advanced research. Moreover, the other dynamic models such as GARCH, TGARCH, PARCH, EGARCH model and different dynamic panel data models such as Areonalo bond could be try to predict the data. Moreover, the other multivariate analysis such as canonical correlation analysis, factor analysis, cluster analysis and discriminant analysis can be done for further research on these data.

Assessing the Temporal Relationship between Race and Ecstasy Use among High School Seniors

2002 ◽  
Vol 32 (3) ◽  
pp. 213-225 ◽  
Author(s):  
George S. Yacoubian
Keyword(s):  
High School ◽  
Temporal Relationship ◽  
High School Seniors ◽  
Time Frame ◽  
Study Data ◽  
Policy Implications ◽  
Chi Square ◽  
Significant Relationships ◽  
The Future ◽  
Monitoring The Future

Previous research has suggested that the use of ecstasy is primarily a white phenomenon. To date, however, these studies have all been conducted at single points in time. No research has examined the temporal relationship between race and the use of ecstasy. In the current study, data collected from 10,088 high school seniors surveyed through the Monitoring the Future (MTF) study between 1996 and 1999 are analyzed. Chi-square statistics are used to explore the temporal relationship between race and the use of ecstasy during this time frame. Statistically significant relationships between race and ecstasy use are discerned. Policy implications are assessed in light of the findings.

scholarly journals Winemaking, Environmental Impacts and Sustainability: New Pathways from Vineyard to Glass?

2020 ◽  
Vol 10 (1) ◽  
pp. 223-243
Author(s):  
Carla Pires Vieira da Rocha ◽  
Eunice Sueli Nodari
Keyword(s):  
Environmental Impacts ◽  
Environmental Problems ◽  
Time Frame ◽  
Human Beings ◽  
Agrifood System ◽  
The Future ◽  
The Relationship

In this text we explore the relationship between vitiviniculture and environment, observing the current conjuncture in which environmental problems are worsening. Taking as a baseline a survey of the literature and as a time frame the 1970s to the present, we begin by examining the development of vitiviniculture from the wider perspective of the contemporary global agrifood system, highlighting in particular the environmental impacts generated by this system. Next, taking into account the panorama of vitiviniculture in Brazil, we turn our focus to notions of sustainability with the aim of outlining possibilities for a reconfiguring of this issue and, at the same time, contextualizing the extent to which the country has been pursuing this direction. We conclude that the future of winemaking depends especially on a more harmonious intervention of human beings in the environment.

scholarly journals LIMITATIONS OF THE PANEL REGRESSION MODEL APPLICATION: THE EXAMPLE OF THE WESTERN BALKAN COUNTRIES

2021 ◽  
Vol 18 (32) ◽  
Author(s):  
Stanko Stanić ◽  
Bojan Baškot
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Time Frame ◽  
Panel Regression ◽  
Time Dimension ◽  
Data Set ◽  
Short Time Series ◽  
Western Balkan Region ◽  
Balkan Region ◽  
Short Time

Panel regression model may seem like an appealing solution in conditions of limited time series. This is often used as a shortcut to achieve deeper data set by setting several individual cases on the same time dimension, where cross units visually but not really multiply a time frame. Macroeconometrics of the Western Balkan region assumes short time series issue. Additionally, the structural brakes are numerous. Panel regression may seem like a solution, but there are some limitations that should be considered.

scholarly journals Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

2020 ◽  
Author(s):  
Ruonan Wang ◽  
Jiancai Du ◽  
Jiangping Li ◽  
Yajuan Zhang ◽  
Jing Wen ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Regression Model ◽  
Influencing Factors ◽  
Minimum Temperature ◽  
Public Awareness ◽  
Arima Model ◽  
Northwest China ◽  
Dynamic Pattern ◽  
Prevention Campaign ◽  
Dynamic Regression Model

Abstract Background: Influenza remains a serious global public health problem and a substantial economic burden. The dynamic pattern of influenza differs considerably among geographic and climatological areas, however, the factors underlying these differences are still uncertain. The aim of this paper is to characterize the dynamic pattern of influenza and its potential influencing factors in Northwest China. Methods: Influenza cases in Ningxia China from Nov. 2013 to Jun. 2020 were served as influenza proxy. Firstly, the baseline seasonal ARIMA model of influenza cases and seasonal pattern were analyzed. Then, the dynamic regression model was used to identifying the potential influencing factors of influenza. In addition, the wavelet analysis was further used to explore the coherence between influenza cases and these significant influencing factors.Results: The high risk periods of influenza in Ningxia presented a winter cycle outbreaks pattern and the fastigium came in January. The seasonal ARIMA(0,0,1)(1,1,0)12 was the optimal baseline forecast model. The dynamic regression models and wavelet analysis indicated that PM2.5 and public awareness are significantly positively associated with influenza, as well as minimum temperature is negatively associated. Conclusion: Meteorological (minimum temperature), pollution (PM2.5) and social (public awareness) factors may significantly associated with influenza in Northwest China. Decreasing PM2.5 concentration or increasing the public awareness prior to the fastigium of influenza may be the serviceable methods to reduce the disease risk of influenza, which have an important implication for policy-makers to choose an optimal time for influenza prevention campaign.

scholarly journals Evaluating U.S. East Coast Winter Storms in a Multimodel Ensemble Using EOF and Clustering Approaches

2019 ◽  
Vol 147 (6) ◽  
pp. 1967-1987 ◽  
Author(s):  
Minghua Zheng ◽  
Edmund K. M. Chang ◽  
Brian A. Colle
Keyword(s):  
Lead Time ◽  
East Coast ◽  
Time Frame ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Winter Storms ◽  
East Coast Winter Storms ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
The Individual

Abstract Empirical orthogonal function (EOF) and fuzzy clustering tools were applied to generate and validate scenarios in operational ensemble prediction systems (EPSs) for U.S. East Coast winter storms. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and Canadian Meteorological Centre (CMC) EPSs were validated in their ability to capture the analysis scenarios for historical East Coast cyclone cases at lead times of 1–9 days. The ECMWF ensemble has the best performance for the medium- to extended-range forecasts. During this time frame, NCEP and CMC did not perform as well, but a combination of the two models helps reduce the missing rate and alleviates the underdispersion. All ensembles are underdispersed at all ranges, with combined ensembles being less underdispersed than the individual EPSs. The number of outside-of-envelope cases increases with lead time. For a majority of the cases beyond the short range, the verifying analysis does not lie within the ensemble mean group of the multimodel ensemble or within the same direction indicated by any of the individual model means, suggesting that all possible scenarios need to be taken into account. Using the EOF patterns to validate the cyclone properties, the NCEP model tends to show less intensity and displacement biases during 1–3-day lead time, while the ECMWF model has the smallest biases during 4–6 days. Nevertheless, the ECMWF forecast position tends to be biased toward the southwest of the other two models and the analysis.

scholarly journals Gender and Age Differences in the Study Plan of University Students

2020 ◽  
Vol 14 (01) ◽  
pp. 62 ◽  
Author(s):  
Hilary I Okagbue ◽  
Sheila A Bishop ◽  
Anjoreoluwa E Boluwajoko ◽  
Adaeze M Ezenkwe ◽  
Glory N Anene ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Undergraduate Students ◽  
Logistic Regression Model ◽  
Simple Random Sampling ◽  
Chi Square ◽  
Ogun State ◽  
Gender And Age ◽  
Study Plan

<p class="0abstract">Effective study plan is a predictor of good academic performance. However, there are few evidences available on the role of gender and age in the study plan for students. This paper investigated the role of gender and age in the adoption of study plan that can guarantee success. A questionnaire was designed and administered to undergraduate students of a world class privately funded university located in Ogun State, Nigeria. Simple random sampling was used and 294 students responded. Chi-square test of independence revealed that gender and age are not associated with frequency of study, study environment, study content preferences and study motivation. There is no Gender difference in the preference of study type, factors that drive, motivation for study and satisfaction with the study plan whereas, age is significantly associated. The logistic regression model was significant and correctly classified 66.3% of satisfaction with the study plan. Gender was not significant and age of students can predict their satisfaction with their study plan. Older students have more odds to be satisfied with their study plan. As students progressed from year one to the final year, they tend to adopt a study plan that can help them obtain high grades and graduate with good result. Artificial Neural Network correctly classified 71.4% of satisfaction using only age as the only factor because, only age contributed significantly to the logistic regression model. Timely academic advising or mentorship is advocated especially for freshers.</p>

scholarly journals Dynamic Regression and Filtered Data Series: A Laplace Approximation to the Effects of Filtering in Small Samples

1996 ◽  
Vol 12 (3) ◽  
pp. 432-457 ◽  
Author(s):  
Eric Ghysels ◽  
Offer Lieberman
Keyword(s):  
Regression Model ◽  
Quadratic Forms ◽  
Mean Squared Error ◽  
Small Sample ◽  
Data Series ◽  
Finite Sample ◽  
Sample Bias ◽  
Squared Error ◽  
Lagged Dependent Variable ◽  
Dynamic Regression Model

It is common for an applied researcher to use filtered data, like seasonally adjusted series, for instance, to estimate the parameters of a dynamic regression model. In this paper, we study the effect of (linear) filters on the distribution of parameters of a dynamic regression model with a lagged dependent variable and a set of exogenous regressors. So far, only asymptotic results are available. Our main interest is to investigate the effect of filtering on the small sample bias and mean squared error. In general, these results entail a numerical integration of derivatives of the joint moment generating function of two quadratic forms in normal variables. The computation of these integrals is quite involved. However, we take advantage of the Laplace approximations to the bias and mean squared error, which substantially reduce the computational burden, as they yield relatively simple analytic expressions. We obtain analytic formulae for approximating the effect of filtering on the finite sample bias and mean squared error. We evaluate the adequacy of the approximations by comparison with Monte Carlo simulations, using the Census X-11 filter as a specific example

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