Social Factors Associated with the Decline in Caries in Brazilian Children between 1996 and 2010

2016 ◽  
Vol 50 (6) ◽  
pp. 551-559 ◽  
Author(s):  
Angelo Giuseppe Roncalli ◽  
Aubrey Sheiham ◽  
Georgios Tsakos ◽  
Georgia Costa de Araújo-Souza ◽  
Richard G. Watt
Keyword(s):  
Panel Data ◽  
Dental Caries ◽  
Human Development ◽  
Social Factors ◽  
Fixed Effects ◽  
Secondary Analysis ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Relationship ◽  
Brazilian Children

Dental caries levels have declined in children since the 1970s in many countries. Most of the postulated main reasons for the decline are speculative and have not been rigorously evaluated. The objective of this study was to assess the relationship between some social factors and the decline in dental caries in Brazilian 12-year-old children from 1996 to 2010. Secondary analysis of national data was performed in 27 Brazilian state capitals. A panel data regression model with fixed effects and multiple linear regression were used to verify the relationship between the explanatory and the dependent variables and also the time-trend effect. The results showed that the DMFT (decayed, missing, and filled teeth) decreased by about 3% per year, and the percentage of caries-free children increased by 4.5% per year. For DMFT and percentage caries free, the results for the panel data regression showed a significant association for the Human Development Index (HDI) in the adjusted model (p = 0.010). When the overall changes over time were compared, the Gini index had a significant association with the overall change in DMFT in the final model of the multiple regression analysis (p = 0.033). Our results indicate that the maintenance of good levels of human development, which includes better education, income, and longevity, are important factors relating to improving levels of oral health in 12-year-old Brazilian children. However, to accelerate this process in cities with the worst caries situation, income inequality should be tackled.

scholarly journals The Effect of Capital Structure on Profitability: An Empirical Panel Data Study

2019 ◽  
Vol 8 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Narinder Pal Singh ◽  
Mahima Bagga
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Fixed Effects ◽  
Regression Models ◽  
Positive Impact ◽  
Stock Exchange ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Individual ◽  
The Relationship

One of the most perplexing issues faced by finance managers is to know about the effect of capital structure on the profitability of firm. Many studies have been carried out to examine the effect of capital structure on the profitability of firms, but most of them belong to other parts of the world, and only few studies have been conducted in India. Thus, the present study has been undertaken to evaluate the effect of capital structure on the profitability of Nifty 50 companies listed on National Stock Exchange of India from 2008 – 2017. The data has been analyzed by using descriptive statistics, correlation and multiple panel data regression models. Four different regression models have been used to study the relationship between capital structure and profitability. In these models, we study the individual effect of total debt and total equity ratios on profitability, that is, ROA and ROE. All four models have been tested with pooled OLS, fixed effects, and random effects. We conclude that there is significant positive impact of capital structure on firm’s profitability.

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

scholarly journals Idiosyncratic Risk and Firm Characteristics on Islamic Stocks of Four Asean Countries 2005–2017

2021 ◽  
Vol 21 (3) ◽  
pp. 1226-1238
Author(s):  
Putri Utami ◽  
Muhammad Budi Prasetyo
Keyword(s):  
Panel Data ◽  
Global Financial Crisis ◽  
The United States ◽  
Idiosyncratic Risk ◽  
Idiosyncratic Volatility ◽  
Firm Characteristics ◽  
Panel Data Regression ◽  
Data Regression ◽  
Asean Countries ◽  
The Relationship

This research investigates idiosyncratic volatility in the Islamic stock of four ASEAN countries, namely Indonesia, Malaysia, Singapore, and Thailand for 2005–2017. The volatility will be analyzed based on the idiosyncratic volatility levels of each country. Furthermore, firm characteristics will be used to determine their relationship to the idiosyncratic volatility movement. This study used the Fama-French Three-Factor model for obtaining the realized value of idiosyncratic volatility. Furthermore, a panel data regression is used to estimate the relationship between firm characteristics and idiosyncratic volatility. The results of this research suggest that mean value of idiosyncratic risk in the Islamic stock of ASEAN countries is below the non-Islamic stock in the United States but above non-Islamic stock in Hong Kong. Meanwhile, after the global financial crisis of 2008, the relationship between return and idiosyncratic risk of Islamic stock changed in all four countries. Panel data regression of firm characteristics shows that firm size is significantly negative in all four countries, while share turnover is insignificant to idiosyncratic volatility.

scholarly journals Analisis Faktor-Faktor yang Mempengaruhi Jumlah Penduduk di Kota Semarang Menggunakan Metode Regresi Data Panel

2019 ◽  
Vol 12 (2) ◽  
pp. 36-41
Author(s):  
Prizka rismawati Arum
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Geographical Area ◽  
Random Effect Model ◽  
Factors Affecting ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Relationship ◽  
Number Of Births

Residents are all people who live in the geographical area of Indonesia for six months or more and or those who have been domiciled for less than six months but aim to settle. Population growth is caused by two components, namely: fertility and mortality. To find out how big the relationship between the  population and the number of births and deaths in each sub-district of Semarang, must observed in several specific time periods and places at once. So in this study, the panel data regression method was used. In panel data regression testing, the results show that the panel data regression model formed to determine the factors that influence the level of population is the random effect model. In this model all assumptions are fulfilled. Significant factors affecting population are number of births. Births and deaths affect the population of 99.95% and the remaining 0.05% is influenced by other factors not examined Penduduk adalah semua orang yang berdomisili di wilayah geografis Indonesia selama enam bulan atau lebih dan atau mereka yang berdomisili kurang dari enam bulan tetapi bertujuan menetap. Pertumbuhan penduduk diakibatkan oleh dua komponen yaitu: fertilitas dan mortalitas. Untuk mengetahui seberapa besar keterkaitan antara jumlah penduduk dengan jumlah kelahiran dan kematian di setiap kecamataan Kota Semarang, harus diamati dalam beberapa periode waktu tertentu dan beberapa tempat secara bersamaan. Sehingga dalam penelitian ini digunakan metode regresi data panel. Dalam pengujian regresi data panel, didapatkan hasil bahwa Model regresi data panel yang terbentuk untuk mengetahui faktor-faktor yang mempengaruhi tingkat jumlah penduduk adalah model random Effect. Pada model tersebut semua asumsi terpenuhi. Faktor yang signifikan mempengaruhi jumlah penduduk adalah jumlah kelahiran. Kelahiran dan kematian mempengaruhi jumlah penduduk sebesar 99.95% dan sisanya sebesar 0.05% dipengaruhi oleh faktor- faktor lain yang tidak di teliti.    

Non-performing Loans and Moral Hazard in the Indian Banking Sector: A Threshold Panel Regression Approach

2020 ◽  
pp. 097215092092613
Author(s):  
Robin Thomas ◽  
Shailesh Singh Thakur
Keyword(s):  
Panel Data ◽  
Moral Hazard ◽  
Regression Model ◽  
Fixed Effects ◽  
Bank Lending ◽  
Empirical Literature ◽  
Data Set ◽  
Panel Data Regression ◽  
Data Regression ◽  
Indian Banks

This article attempts to examine the effect of non-performing assets (NPA) on behaviour of banks in India. The objectives of this article is to test if lending choices of Indian Banks demonstrate moral hazard and to test whether an increase in NPA ratio of banks raises riskier bank lending. We employ a threshold panel data regression model on a data set retrieved from the Reserve bank of India, which covered 45 commercial banks during the period 2009–2015, to test if lending choices of Indian banks demonstrate moral hazard. The results establish that the moral hazard hypothesis does not hold true for the given sample of India Banks, suggesting that an increase in the NPA ratio does not potentially increase riskier lending in sample banks. We find empirical evidence for the notion that ‘too-big-to-fail’ banks possibly have certain incentives to take higher risks and thus have higher NPA ratios. Graphical approach to NPA threshold explanation reveals presence of threshold; however, it could not be statistically established. Future implications of findings are evaluated. The study seminally adds to the empirical literature on use of fixed effects threshold panel data regression model in the context of Indian banks.

Islamic banks’ fee income, characteristics and risk: Panel data analysis evidence from Indonesia

2018 ◽  
Vol 6 (1) ◽  
pp. 6 ◽  
Author(s):  
Siti Sarah Mat Isa ◽  
Masturah Ma’in ◽  
Azlina Hanif
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Operating Income ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Us ◽  
The Relationship

One of the non-operating income in Islamic banking operation, which is fee income has become progressively vital in expanding their income to counter decreasing net earnings due to rivalry from other financial competitors. However, it is important for Islamic banks to find out any potential risk that will distress their performance due to this activity. This is because, mixed results on this issue derived from the previous studies especially in the Western context such as in the US, Germany and other European countries. Using Indonesian Islamic bank’s quarter data between 2009 and 2013, this study adopts the panel data regression analysis to examine the relationship between Indonesian Islamic banks fee income and risk. The empirical results signified that fee income activities able to reduce Indonesian Islamic bank’s risk.  

scholarly journals Capital Structure of Companies Listed at the Warsaw Stock Exchange and the COVID-19 Pandemic Effect on Their Risk

2021 ◽  
Vol 4 (32) ◽  
pp. 153-166
Author(s):  
Jerzy Gajdka ◽  
Marek Szymański
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Financial Management ◽  
Stock Exchange ◽  
Idiosyncratic Risk ◽  
Total Risk ◽  
Panel Data Regression ◽  
Data Regression ◽  
Warsaw Stock Exchange ◽  
The Relationship

Subject: The financial management of companies is examined in the context of the COVID-19 pandemic. Specifically, the relationship between their capital structure and risk changes during the pandemic is scrutinised. The purpose of the article: To determine how companies’ total, systematic and idiosyncratic risks changed during the COVID-19 pandemic depending on their capital structure based on a sample of organisations listed at the Warsaw Stock Exchange. Methodology: The study involves the use of a panel data regression model. Results of the research: The COVID-19 pandemic had an impact on the risk of overleveraged companies and underleveraged ones alike. Its influence on their total risk was weaker among the underleveraged organisations. Regarding systematic risk, its levels did not generally change significantly in the wake of the pandemic, but idiosyncratic risk, only in the case of the overleveraged companies increased statistically significantly.

scholarly journals Estimation of Indonesia Wooden Furniture Export Demand Function

2021 ◽  
Vol 9 (3) ◽  
pp. 357-367
Author(s):  
Hanna Sri Meiliani Uli Simangunsong ◽  
Bintang Charles Hamonangan Simangunsong ◽  
Elisa Ganda Togu Manurung
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Demand Function ◽  
Relative Price ◽  
Export Demand ◽  
Fixed Effects Model ◽  
Price And Income Elasticities ◽  
Panel Data Regression ◽  
Data Regression ◽  
Wooden Furniture

The export value of Indonesia’s wooden furniture was sharply decreased by about 31.9% over the period in 2007-2018. On the other hand, global wooden furniture export was increased by 5.8% during the same period. Understanding the behavior of the demand side of Indonesia’s wooden furniture exports that is reflected by its relative price and income elasticities is needed for the policy development of Indonesia’s wooden furniture industry in the future. The objective of this study was to estimate the export demand function of Indonesia wooden furniture using a panel data regression model. Three types of panel data models, such as pooled ordinary least squares model, fixed-effects model, and random effects model, were investigated. The results showed that the export demand function of Indonesia wooden furniture could be well estimated using the fixed effects model. Relative price elasticity and income elasticity were -0.45 and 0.8, respectively. The adjusted R2 value obtained was 0.99. Keywords: export demand function, panel data regression, wooden furniture

scholarly journals Country innovative development: impact of the shadow economy

2020 ◽  
pp. 41-49
Author(s):  
Alina Vysochyna ◽  
Olena Kryklii ◽  
Mariia Minchenko ◽  
Aygun Akbar Aliyeva ◽  
Kateryna Demchuk
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Fixed Effects ◽  
Shadow Economy ◽  
Slovak Republic ◽  
Panel Data Analysis ◽  
Innovative Development ◽  
State Management ◽  
Panel Data Regression ◽  
Data Regression

This article generalizes arguments and counterarguments within the scientific discussion regarding the determination of the influence of illegal economic activity and expansion of the shadow economy on innovative country development. The systematization of the scientific works on the above problems proves that there is no one no complexity and unity in the above-mentioned scientific findings, which, in turn, demonstrates the necessity of further theoretical and empirical search in this sphere. Thus, it was developed a scientific hypothesis about the negative influence of the shadow economy on innovative country development. In order to test this hypothesis it was developed a scientific and methodological approach that consists of several stages: 1) correlation analysis in order to eliminate multicollinearity problem between control variables; 2) analysis of dataset descriptive statistics; 3) running Hausman test in order to clarify specification of the regression model (fixed or random effects model); 4) realization of the panel data regression analysis for the whole country sample and separately for Ukraine, characteristics of its results. Technically all stages of the research are realized with the help of Stata 12/S.E. software. The country sample consists of 9 countries (Azerbaijan, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic, Slovenia, and Ukraine). Time horizon – 2008-2018. Running of the panel data regression analysis (model specification – with fixed effects) allow confirming research hypothesis for the whole country sample (an increase of shadow economy negatively affected innovative country development: an increase of shadow economy to GDP ratio in 1 % leads to the decrease of the Global Innovation Index in 0.5 points). However, it was not proved for Ukraine separately. It leads to the conclusion that innovative development in Ukraine does not highly dependent on the shadow economy scale because of more significant obstacles on the way to innovation adoption (institutional inefficiency, regulatory drawbacks, etc.). Keywords: innovative economic growth, innovative state management, panel data analysis, shadow economy.

scholarly journals Modeling Human Development Index of Bali with Spatial Panel Data Regression

2019 ◽  
Vol 4 (5) ◽  
pp. 132-137
Author(s):  
Mita Lasdiyanti ◽  
Eka N. Kencana ◽  
Putu Suciptawati
Keyword(s):  
Panel Data ◽  
Human Development ◽  
Human Development Index ◽  
Panel Data Analysis ◽  
Spatial Error ◽  
Development Index ◽  
Spatial Panel ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data

Human development index (HDI) is an index that represents the successfulness of human development in a region. For Bali, one of 34 provinces in Indonesia, the progress of HDI in the period 2010–2017 showed an increasing trend. In the year 2010, the Bali’s HDI is accounted for 70.10, gradually increase to 74.30 in the year 2017. However, in 2017 there are some regions with their HDIs are below of Bali’s HDI, namely Jembrana, Buleleng, Klungkung, Bangli, and Karangasem. The aim of this work is to model the HDI of 9 regencies of Bali so that the main determinant to increase the HDIs especially for the regencies with lower HDIs could be determined. The model consists of one dependent variable (HDI) with three indicators as the independent ones, there are (a) life expectancy, (b) education, and (b) standard of living. By applying spatial panel data analysis, five models were built i.e. CEM, FEM (individual), FEM (time), REM, and spatial error FEM to determine the effect of each indicator. The result shows the best model is spatial error FEM in which education has the biggest influence compare than the others.

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