scholarly journals DETERMINANT OF CAPITAL RATIO: A PANEL DATA ANALYSIS ON STATE-OWNED BANKS IN INDONESIA

2014 ◽  
Vol 16 (4) ◽  
pp. 369-386 ◽  
Author(s):  
Pamuji Gesang Raharjo ◽  
Dedi Budiman Hakim ◽  
Adler Haymans Manurung ◽  
Tubagus Nur Ahmad Maulana
Keyword(s):  
Panel Data ◽  
Panel Data Analysis ◽  
Banking System ◽  
Interest Rate Risk ◽  
Management Capability ◽  
Panel Data Regression ◽  
Data Regression ◽  
Structure State ◽  
The Interest Rate

Capital plays important role to support the operational of the banks and to create a sound banking system in aggregate. For this reason, the banks are required to have a sufficient amount of capital, both to support its business expansion as well as a buffer to prevent and to absorb any unexpected losses. This paper analyzes determinants of capital ratio of the state-owned banks in Indonesia. Using panel data regression model, the result shows that the capital ratio of these state-owned banks is affected by the size of the bank, the bank’s leverage, the quality of management, and the interest rate risk. Contrary to the existing literatures, this paper does not support the effect of management capability to generate income on the bank’s capital ratio. Keywords: Capital structure, state-owned banks, panel estimation.JEL Classification: C23, G21, G32

scholarly journals DETERMINANT OF CAPITAL RATIO: A PANEL DATA ANALYSIS ON STATE-OWNED BANKS IN INDONESIA

2014 ◽  
Vol 16 (4) ◽  
pp. 395-414 ◽  
Author(s):  
Pamuji Gesang Raharjo ◽  
Dedi Budiman Hakim ◽  
Adler Haymans Manurung ◽  
Tubagus Nur Ahmad Maulana
Keyword(s):  
Panel Data ◽  
Panel Data Analysis ◽  
Banking System ◽  
Interest Rate Risk ◽  
Jel Classification ◽  
Panel Data Regression ◽  
Structure State ◽  
Asset Growth ◽  
The Stability ◽  
Level Of Significance

Capital has an important role in maintaining safety of banks and in order to create a sound banking system. Banks are required to have a sufficient amount of capital, both to support its business expansion as well as a buffer to prevent any unexpected loss that banks might face and absorb losses arising from a variety of risks. Eventhough consists of four banks, State owned banks in Indonesia are catalystor for the banking industry in Indonesia. The failure of state-owned banks can affect the stability of Indonesian banking system. This study aims to study and analyze determinants of capital ratio of state-owned banks. Several variables have been used in previous studies to be used a proxy. The study applied panel data regression model. The capital ratio of state-owned banks is affected by asset growth (LNSIZE), equity to total liabilities ratio (EQTL), non performing loan (NPL), interest rate risk (IRR), and operational cost to operational revenue ratio (BOPO) on a different level of significance.  Keywords: Capital structure, state-owned commercial banks, panel data JEL Classification: C23; G21; G32

scholarly journals Market Concentration, Market Share, and Profitability (Study at Indonesian Commercial Banking in the Period of 2001-2012)

2015 ◽  
Vol 11 (27) ◽  
pp. 18
Author(s):  
Sapto Jumono ◽  
Noer A. Achsani ◽  
Dedi B. Hakim ◽  
Muhamad Fidaus
Keyword(s):  
Panel Data ◽  
Market Share ◽  
Concentration Ratio ◽  
Panel Data Analysis ◽  
Positive Influence ◽  
Commercial Banking ◽  
Panel Data Regression ◽  
Data Regression ◽  
Individual Market ◽  
Market Channel

<p>The objective of this research is to examine the influence of market structure on Indonesian commercial banking performance by using concentration ratio and individual market share through deposits market channel and credits market channel. There were 101 banks chosen from 120 banks in a period of 2001-2012 as sampling of research by using purposive sampling. This research uses data panel that combines data cross section and data time series, therefore panel data regression is used in this research. The result of panel data analysis has allowed us to conclude that concentration ratio of deposits market has a significant and positive influence on ROA, meanwhile concentration ratio of credits market, individual market share of deposits, and individual market share of credits market have no significant effects on ROA.</p>

scholarly journals The Impact of Multi-Disciplinary Input on Glycaemic Control Over Time in Children on Intensive Insulin Therapy Using Real World Prospectively Collected Data

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A454-A455
Author(s):  
Jason Foran ◽  
Susan M O’Connell ◽  
Declan Cody ◽  
Eric Somers
Keyword(s):  
Panel Data ◽  
Glycaemic Control ◽  
Panel Data Analysis ◽  
Daily Injection ◽  
Matched Pairs ◽  
Panel Data Regression ◽  
Data Regression ◽  
Treatment Type ◽  
The Impact ◽  
Over Time

Abstract Aims: To investigate the factors impacting on glycaemic control over time including treatment type, educational input and patient demographics within an Irish tertiary paediatric diabetes centre. Methods: Using a prospectively maintained database of clinical encounters, data was analysed in age matched pairs from 2007 to 2019. Pairs were matched by insulin treatment type (pump v multiple daily injection (MDI)). Matching was performed on the basis of gender, current age, age at diagnosis and HbA1c at pump commencement. Panel data regression was performed on the entire sample and analysed for the impact of differing insulin regimens by gender, age and duration of diagnosis. This model was then used to assess the impact of intensive re-education sessions on HbA1c. Results: From 999 patients there were 104 matched pairs. Compared to MDI, matched pump patients had a lower HbA1c 6 months after commencement [Difference in HbA1c = 0.60% p &lt;0.01], this effect persisted to 8 years [0.57% p=0.01]. Panel data analysis showed CSII therapy reduces HbA1c by 0.57% relative to MDI therapy (p&lt;0.001). Patients who required intensive re-education showed a HbA1c 0.91% greater than otherwise identical patients prior to re-education, after these sessions HbA1c drops by a statistically significant 0.79% (p&lt;0.001). Conclusions: Compared to matched peers on MDI treatment regimens, patients on pump therapy showed significant improvements in HbA1c which was an effect sustained up to 8 years. Panel data regression confirms these findings and in addition shows that intensive re-education is associated with a significant drop in previously elevated HbA1c levels.

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 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 Non-performing financing of Islamic rural bank industry in Indonesia

2019 ◽  
Vol 14 (1) ◽  
pp. 20-28 ◽  
Author(s):  
Muhamad Nadratuzzaman Hosen ◽  
Syafaat Muhari
Keyword(s):  
Panel Data ◽  
Government Policy ◽  
Negative Impact ◽  
Positive Impact ◽  
Its Region ◽  
Efficiency Ratio ◽  
Return On Equity ◽  
Panel Data Regression ◽  
Data Regression

This study is aimed at analyzing the financial performance and indicator of macroeconomics to influence the quality of financing at Islamic Rural Bank Industry (IRBI) in Indonesia. The panel data regression is used to predict the change of quality of financing which is reflected by value of non-performing financing (NPF). The model of this study is grouped by four areas of working zones because IRBI has different competency depending on its region. The sample of the study used 72 IRBIs in the periods of Quarter II 2010 to Quarter I 2016. The results of the study show that simultanuously variables for the size of banks, financing to deposit ratio (FDR), operational efficiency ratio (OER), return on equity (ROE), expense to assets (EA), percentage of gross domestic product (GDP), and the rate of inflation are statistically significant to non-performing financing of the IRBI in Indonesia. GDP has strongly significant impact on the NPF of IRBI in Indonesia. According to Areas of working zones, inflation has quite significant impact on the IRBI in Zone One, and GDP has strongly significant impact on the IRBI in Zone Two, Zone Three, and Zone Four. Nevertheless, there are different effects of GDP towards NPF which has a negative impact on Zone One and Zone Four, meanwhile Zone Two and Zone Three have positive impact. In conclusion, government policy treatment should be different at every zone.

scholarly journals Emprical Analysis of Competition Dynamics in Turkish Banking via Competition Determination Approaches

2013 ◽  
Author(s):  
Utku Altunöz
Keyword(s):  
Panel Data ◽  
Market Structure ◽  
Banking Sector ◽  
Banking System ◽  
Regression Method ◽  
Competitive Behavior ◽  
Market Conditions ◽  
Business World ◽  
Panel Data Regression ◽  
Data Regression

Due to the complex and close interaction of banks with other economic units, any trouble in banking sector might have repercussion on the whole economy which makes the market structure and competition in banking sector as a cynosure. Business world is facing gradually increasing competition. It seems that the existence of firms depends on the power and the advantage of their competitiveness. The purpose of this study is to analyze the competition structure and the market conditions of Turkish banking system. despite the existence of a number of studies about competition in banking sector, there is still a lack of the studies which has been done with Lerner's Index. Due to this fact, Lerner’s Indeks is used in this study. Bank level determinants of Lerner Index is analysed using Panel Data Regression Method and was reached to factors effecting competitive behavior in Turkish Banking Sector.

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.

scholarly journals Pengaruh Intellectual Capital Terhadap Return On Asset Pada Bank Umum Syariah di Indonesia Periode Q1.2013 - Q4.2014

2017 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Umi Muhanik ◽  
Dina Fitrisia Septiarini
Keyword(s):  
Panel Data ◽  
Intellectual Capital ◽  
Panel Data Analysis ◽  
Secondary Data ◽  
Sampling Technique ◽  
Significance Test ◽  
Return On Assets ◽  
Financial Report ◽  
Panel Data Regression ◽  
Data Regression

ROA) sharia banks Q1.2013 - Q4.2014 period. The method used is quantitative method with jenuh sampling technique. This study uses 11 Islamic banks and the technique used is the panel data regression. The process of calculating this study using Eviews 8.0 program. Independent variables used Intellectual Capital is VACA, VAHU, STVA and dependent variables use Return on Assets (ROA). The data used is secondary data with the collected data quarterly financial report for the period Q1.2013 – Q4.2014.Hypothesis testing is done using simultaneous significance test (F test) and partial significance test (t test). Panel data analysis conducted showed that simultaneous VACA, VAHU, STVA significant effect on ROA while partial VACA no significant effect on ROA, VAHU significant effect on ROA, STVA no significant effect on ROA.

scholarly journals The Determinant of Financial Performance of Indian Public Sector Banks- A Panel Data Approach

2020 ◽  
Vol 11 (5) ◽  
pp. 285
Author(s):  
Vikram Jeet ◽  
Parvesh Kumar Aspal
Keyword(s):  
Panel Data ◽  
Financial Performance ◽  
Research Work ◽  
Banking System ◽  
Capital Adequacy ◽  
Public Sector Banks ◽  
Panel Data Regression ◽  
Data Regression ◽  
Earning Quality ◽  
Asset Quality

In the accelerated development of an economy, the role of a vibrant banking system and financial structure is considered as highly indispensable. The banking sector is recognized as an important element to portrait the financial and economic strength of a country. The economic importance of the banking system may be considered in the form of capital formation, inspiring innovation, monetization, and facilitator of monetary policy. The present research work investigates the association between banks' profitability and the banks’ specific factors of Indian Public Sector Banks. The research work is based on secondary data drawn from annual reports of banks from the period of 2015 to 2019. The panel data regression statistical technique has been employed to vindicate the influence of explanatory variables viz. Capital Adequacy, Human Capital, Liquidity, Management Efficiency, Asset Quality, and Earning Quality, which have been employed as independent variables and Return on Equity, as the dependent variable. Panel data regression model results have reported that the regression coefficients are found statistically significant and the high value of adjusted R- square expresses the overall best fit of the fixed effects model. A significant positive relationship has been found between the financial performance of bank (ROE) and human capital, liquidity, management efficiency, and asset quality. Whereas capital adequacy and earning quality of the banks have an insignificant impact on the profitability of banks. Hence, the financial performance evaluation enables the banks to analyze their financial strength and to follow necessary protective initiatives for its sustainability.

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