FRACTIONAL REGRESSION MODEL FOR INVESTIGATING THE DETERMINANTS OF THE UNEMPLOYMENT RATES IN OECD COUNTRIES

2021 ◽  
Vol 21 (2) ◽  
pp. 423-428
Author(s):  
TUBA KOÇ ◽  
EMRE DÜNDER ◽  
HAYDAR KOÇ
Keyword(s):  
Growth Rate ◽  
Panel Data ◽  
Regression Model ◽  
Regression Models ◽  
Unemployment Rates ◽  
The World ◽  
Import And Export ◽  
Saving Rates ◽  
Potential Factors ◽  
Determinants Of Unemployment

Unemployment is a serious problem for all over the world. It is a crucial task to endeavor with the unemployment for the welfare of the world. Once, the potential factors should be known to accomplish this task. The aim of this study is to investigate the determinants of the unemployment rates using fractional regression models for the 35 OECD (Organization for Economic Co-operation and Development) countries over the periods 2000-2017. We determined the factor affecting the unemployment rate by the fractional regression model using GMMbgw and GMMpre estimators for panel data. The empirical results revealed the significant determinants of unemployment as the result of the fractional regression models. Finally, we observe that saving rates, the growth rate of import and export are expressive on the unemployment rates

scholarly journals A Machine Learning based approach to unleash the impact of COVID-19 on Indian Stock Market

2020 ◽  
Author(s):  
Nusrat Rouf ◽  
Majid Bashir Malik ◽  
Tasleem Arif
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Regression Model ◽  
Regression Models ◽  
Pearson Correlation ◽  
Mean Square ◽  
Random Forest Regression ◽  
The World ◽  
Closing Price ◽  
The Impact

Abstract Introduction: Advancement in information technology, be it hardware, software or communication technology, over few decades has rapidly impacted almost every field of study. Machine learning tools and techniques are nowadays applied to every field. It has opened the ways for interdisciplinary research by promising effective analyzation and decision-making strategies. COVID-19 has badly affected more than 200 countries within a short span of time. It has drastically affected both daily activities as well as economic activities. Herd behavior of investors has triggered panic selling. As a result, stock markets around the world have plunged down.Methods: In this paper, we analyze the impact of COVID-19 on NSE (National Stock Exchange) index Nifty50. We employ Pearson Correlation and investigate the impact of total confirmed cases and daily cases on Nifty50 closing price. We use various machine learning regression models for predictive analysis viz, linear regression with polynomial terms (quadratic, cubic), Decision Tree Regression and Random Forest Regression. Model performance is measured using MSE (Mean Square Error), RMSE (Root Mean Square Error) and R2 (R Squared) evaluators. Results: Correlation analysis reveals that total confirmed cases and daily cases in both India and the World have negative correlation with Nifty50 closing prices. Moreover, Nifty50 closing prices are more negatively correlated with total confirmed and daily cases in India. Predictive analysis shows that the Random Forest Regression model outperforms all other models. Conclusion: We analyze and predict the impact of COVID-19 on closing price of Nifty50 index. We employ Pearson Correlation and investigate the impact of COVID-19 on Nifty50 closing prices. We use various machine learning regression models to predict the closing price of Nifty50 index. Results reveal that the market volatility is directly proportional to increase in number of COVID-19 cases. Random Forest Regression model has comparatively shown better RMSE and R2 values.

Determinants Of Capital Structure For Multinational And Domestic Companies In India: Panel Data Regression Model

2019 ◽  
Vol 118 (7) ◽  
pp. 147-154
Author(s):  
K. Maheswari ◽  
Dr. J. Gayathri ◽  
Dr. M. Babu ◽  
Dr.G. Indhumathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Regression Model ◽  
Business Risk ◽  
Return On Assets ◽  
Panel Data Regression ◽  
Data Regression ◽  
Return On Capital ◽  
Domestic Companies ◽  
The Capital Structure

The capital structure refers to the components of capital needed to establish and expand its business activities. The study was made with an objective to examine the determinants of capital structure of multinational and domestic companies listed in S&P BSE automobile sector. The study concluded that there is significant impact on capital structure determinants such as size, business risk, non debt shield tax, return on assets, tangibility, profit, return on capital employed and liquidity on the capital structure of multinational and domestic companies of Indian Automobile Sector.  

Changing Composition and Growth Performance of Services Sector in India after Liberalization

2020 ◽  
pp. 432-436
Keyword(s):  
Growth Rate ◽  
Growth Performance ◽  
Direct Investment ◽  
Service Sector ◽  
World Market ◽  
Limiting Factor ◽  
Arunachal Pradesh ◽  
Services Sector ◽  
State Economy ◽  
The World

This present study makes an analysis of changing contribution of sub-sector and composition and growth performance in Indian economy. In addition to that, the contribution of sub-sector of service sector in state economy. The results revealed that the growth rate of Chandigarh was high due to providing especial emphasis on dominating sub-sectors of services and its most preferred destination for technology whereas, Sikkim and Arunachal Pradesh due to geographical and environmental conditions development were higher in floriculture and agriculture, although, tourism emerged as a new profession and have different opportunities. Apart of that, in the wake of some challenges in the form of lack of infrastructure, recent crisis in the world market, foreign direct investment (FDI) restrictions and outsourcing backlash were major limiting factor.

scholarly journals Withdrawal Properties of Self-Tapping Screws in Japanese larch (Larix kaempferi (Lamb.) Carr.) Cross Laminated Timber

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 524
Author(s):  
Junhua Xu ◽  
Shuangbao Zhang ◽  
Guofang Wu ◽  
Yingchun Gong ◽  
Haiqing Ren
Keyword(s):  
Regression Model ◽  
Engineering Design ◽  
Wood Species ◽  
Japanese Larch ◽  
Effective Length ◽  
Larix Kaempferi ◽  
Cross Laminated Timber ◽  
The World ◽  
Different Types ◽  
Length Increment

With the increasing popularity of cross-laminated timber (CLT) constructions around the world, there have been attempts to produce CLT using local wood species in different countries, such as Japanese larch (Larix kaempferi (Lamb.) Carr.) in China. Thus, the need to investigate the connection performance also increases to support the design and construction of CLT buildings using local wood species. In this study, the withdrawal properties of three different types of self-tapping screws (STS), with a diameter of 6 mm, 8 mm, and 11 mm, were tested with Japanese larch CLT. The results revealed that the withdrawal strength of STS increased with increasing density and effective length, but decreased with an increasing diameter. With a density increment of 0.05 g/cm3, the withdrawal strength increased by an average of 9.4%. With an effective length increment of 24 mm, the withdrawal strength increased by an average of 1.4%. An empirical regression model was adopted to predict the withdrawal strength of Japanese larch CLT based on the results, which can be used for potential engineering design of CLT connections using STS.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

Plastics: The Discovery in the World and Development in China

2013 ◽  
Vol 750-752 ◽  
pp. 811-815
Author(s):  
Ya Xi Jiang ◽  
Meng Jiang
Keyword(s):  
Export Trade ◽  
John Wesley ◽  
Advanced Level ◽  
Chinese Characteristics ◽  
The World ◽  
Import And Export ◽  
Plastic Industry ◽  
Plastics Industry ◽  
American Scientist

Alexander Parkes found the earliest plastic in 1850. American scientist John Wesley Hyatt achieved the first patent of plastic (1970) and inaugurated the first plastics industry (1873) with his brother in the world. From then on, plastics industry all over the world have experienced about 150 years development. Based on the learning from overseas industries, China gradually constructed and cultivated himself plastics industry system that is full of Chinese characteristics. The amount of plastics production, plastics products and plastics machine production as well as plastics consumption in China increased quickly. The value of plastics import and export trade rose year by year. Nowadays, China reaches an advanced level in the world no matter plastics machine production, plastic goods production, plastics consumption, or outlet of plastics machines and products. Plastic industry has be one of the important light manufacturing pillar industries in society and economics development of China.

Recent trends in import and export of Rudraksha beads in India

2018 ◽  
Vol 25 (4) ◽  
pp. 225-228
Author(s):  
Amar Nath Singh ◽  
Keyword(s):  
Electrical Properties ◽  
Present Article ◽  
High Demand ◽  
The World ◽  
Recent Trends ◽  
Import And Export ◽  
Prayer Beads ◽  
Religious Importance ◽  
Magnetic And Electrical Properties

The Rudraksha beads are traditionally used as prayer beads in Hinduism (especially Shaivism) throughout India. Apart from the religious importance, medicinal, bio-magnetic and electrical properties of the Rudraksha beads have also been reported. This commodity is in high demand from the devotees across the world. Therefore, this is in trade throughout the country and abroad. The recent trends in import and export of Rudraksha beads in India have been described in the present article, considering scant publications on this aspect.

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

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