GREEN GROWTH: IMPORTANT DETERMINANTS

2015 ◽  
Vol 60 (02) ◽  
pp. 1550014 ◽  
Author(s):  
GHULAM SAMAD ◽  
RABIA MANZOOR
Keyword(s):  
Time Series Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Developed Countries ◽  
Fixed Effect Model ◽  
Least Square ◽  
Series Data ◽  
Market Size ◽  
Green Growth ◽  
Effect Model

We discuss the important determinants requires to develop green patents, which eventually reinforce green growth. The theoretical framework examined four elements, the enforcement of intellectual property rights (IPRs), research and development (R&D) expenditures, market size and environmental taxations. We empirically test the green patent data to test the interrelationship of green patents representing the green innovations and IPR, R&D expenditures, market size and environmental taxations. Keeping in view the availability of the data we studied 11 developed countries, which are Austria, Australia, Canada, France, Japan, Finland, Germany, Sweden, U.K and U.S. The panel data can better handled the technological change rather than the pure cross section or pure time series data. Therefore, this study used the Pooled Least Square estimation techniques like Fixed Effect Model (FEM) and random effect model (REM) for both balance period of 1995–2010 and unbalanced period from 1995–2010. We only interpreted the balance period results depicting the enforcement of IPRs has negative and significant impact on green patents while the R&D expenditures, market size and environmental taxations has positive and significant impact on the green patents e.g. development of green innovations. We believe that the enforcement of explanatory variables will eventually acquire green growth.

scholarly journals Company Internal Factors and Their Influences on Financial Performance

2021 ◽  
Vol 3 (2) ◽  
pp. 177
Author(s):  
Lilis Renfiana ◽  
Yudhisthira Ardana
Keyword(s):  
Financial Performance ◽  
Firm Size ◽  
Time Series Data ◽  
Stock Exchange ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Series Data ◽  
Fixed Costs ◽  
Effect Model

This research aims to systematically, actual, and accurately explain the facts and characteristics of the company and their effect on financial performance. Data in the form of time-series data from 2015-2019 and cross-section data collected from the financial statements of automotive companies listed on the Indonesia Stock Exchange then obtained nine companies that meet the criteria. The independent variables are Firm Size, Leverage, Liquidity, and the dependent variable is financial performance as proxied by Return On Equity (ROA). The research used panel data techniques; Common Effect Model, Fixed Effect Model, and Random Effect Model. The results show that Firm Size partially has a negative and significant effect, meaning that the greater the assets owned by the company, the more complex the agency problems faced. The partial leverage variable has a negative and significant effect, means that the use of relatively high debt will cause fixed costs in the form of interest expenses and loan principal installments to be paid, the greater the fixed costs. The liquidity variable partially has a positive and insignificant effect. This means that changes that occur in both the number of current assets or current liabilities affect increasing profits so that the increase in Liquidity (CR) or the level of liquidity affects changes in increasing company performance (ROA).

scholarly journals Testing The Warrants Mispricing and Their Determinants: The Panel Data Models

2017 ◽  
Vol 5 (2) ◽  
pp. 118
Author(s):  
Muhammad Rizky Prima Sakti ◽  
Abdul Qoyum
Keyword(s):  
Panel Data ◽  
Stock Return ◽  
Time Series Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Daily Basis ◽  
Series Data ◽  
Effect Model ◽  
The Impact

This paper empirically studied the impact of several variables such as moneyness, stock return, maturity, and volatility on the warrant mispricing. We selected 4 companies listed in Bursa Malaysia such as MHC Plantations Bhd, MKH Bhd, YFG Bhd, and UNISEM to investigate the mispricing of warrants. Subsequently, panel time series data employed with daily basis from 30 June 2010 until 30 June 2013. The Black-Scholes Option Pricing Model (BSOPM) used to determine the mispricing of warrant. Several panel data techniques employed in this study such as pooled-OLS, fixed effect model (FEM), and random effect model (REM). In turn, we found that FEM is well explained the determinants of warrant mispricing. Thus, empirical results suggest that moneyness, maturity, and volatility are positively and significantly explained the mispricing of warrant, while stock return does not give an impact toward the warrant mispricing. The BSOPM is consistently mispricing the warrant either in-the-money (ITM) or out-the money (OTM) warrants. The market is not efficient on the warrants traded for four companies observed

Analysis of Board Size and Firm Performance: Evidence from NSE Companies Using Panel Data Approach

2016 ◽  
Vol 9 (2) ◽  
pp. 148-172 ◽  
Author(s):  
Anjala Kalsie ◽  
Shikha Mittal Shrivastav
Keyword(s):  
Firm Performance ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Least Square ◽  
Empirical Literature ◽  
Board Size ◽  
Effect Model

This article seeks to examine the relationship between the board size and firm performance. Existing literature on board size is based on different theories of corporate governance. While agency theory and resource dependency theory suggest that the board size positively affects performance, stewardship theory favours smaller board size and argues that larger board size negatively impacts the firm performance. The present article adds to the empirical literature by employing panel data analysis of 145 non-financial companies listed in the NSE CNX 200 Index of India corresponding to 16 industries. The study is carried out for a period of five years from 2008 to 2012. The firm performance has been measured using Tobin’s Q and the market-to-book value ratio (MBVR) as market-based measures and return on assets (ROA) and return on capital employed (ROCE) as accounting-based measures. The fixed effect model, random effect model and feasible generalised least square (FGLS) regression models are applied to achieve the above-mentioned objectives. The results conclude that the board size has a positive and significant impact on the firm performance.

scholarly journals Is MGNREG Scheme Complementary to Fishing Activities?

2016 ◽  
Vol 5 (1) ◽  
pp. 47-53
Author(s):  
Chiranjib Neogi ◽  
Kamal Ray ◽  
Ramesh Chandra Das
Keyword(s):  
Freshwater Fish ◽  
Rainwater Harvesting ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Component Part ◽  
Indian States ◽  
Effect Model

Freshwater fish output is taken as a proxy variable for empirical assessment of indirect benefits in terms of enhanced quantity of freshwater fish (output) cultivation. It is not unlikely to assess empirically the productivity of subsidized public scheme when rural development or rural asset generations are underlined in the said scheme, MGNREG Act, 2005. Rainwater harvesting is a major component part of the scheme since about 49.5 per cent of the total fund is already utilized on water conservation and obviously it has an impact on the cultivation of freshwater fish output. Time series data on annual expenditure on MGNREG and corresponding freshwater fish output at the state level are taken during the period 2006-07 to 2013-14 for 16 major Indian states. Fixed effect model and random effect models are being applied and the Hausman specification test suggests that fixed effect model is more appropriate than random effect model. Significant differences among the intercepts of the selected states are revealed as per F test. The results of fixed effect panel regression establish that fish output is enhanced by 0.000257 thousand tones or 0.26 tones if MGNREG expenditure rises by one crore or 10 million rupees. 

scholarly journals Inequality and Economic Growth: An Analysis of 8-Panels

2017 ◽  
Vol 3 (2) ◽  
pp. 173
Author(s):  
Khadijah A. Idowu ◽  
Yusuf Bababtunde Adeneye
Keyword(s):  
Economic Growth ◽  
Panel Data Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Specific Effect ◽  
Developed Countries ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Effect Model ◽  
The World

<p><em>Purpose: This paper investigates the effects of inequality on economic growth in the world using continental approach.</em><em></em></p><p><em>Design/methodology:<strong> </strong>Gini Coefficient and Gross Domestic Products (GDP) per capita were used to measure inequality and economic growth respectively. The study conducted a panel data analysis of the relationship between inequality and economic growth. The data span from 1991-2015. Five countries were selected each from seven continents and were also pooled together to constitute a single panel for 35 countries, thus establishing 8 panels. The Hausman test was conducted to determine whether a random or fixed effect model best fit pooled countries analysis or not.</em><em></em></p><p><em>Findings: Findings revealed that for the developing countries, high income inequality retards economic growth while for the developed countries such as Europe countries; the situation seems to be different. European countries as revealed in the findings showed that developed countries have benefited from inequality which has significantly and positively affected their economic growth. The results for Panel II (Asia countries) and Panel III (Europe countries) are in line with the study of Forbes (2000) and Li and Zou (1998) that documented that inequality boosts economic growth. Importantly, we found that inequality positively affects economic growth for Panels/Continents with fixed effect model while inequality negatively affects economic growth for Panels/Continents with random effect model.</em></p><p><em>Research Limitation: The study did not control for each continent differences. For African countries, weak institutional settings and environment is a key factor contributing to high inequality.</em><em></em></p><p><em>Originality: The paper was able to know the specific effect of inequality on economic growth in each continent in the World. This documents continents that have benefited from inequality and those that inequality has greatly affected their economies negatively.</em><em></em></p>

scholarly journals COMPARISON OF VILLAGE DEVELOPMENT BEFORE AND AFTER VILLAGE FUND IMPLEMENTATION IN NAGAN RAYA DISTRICT

2021 ◽  
pp. 18-35
Author(s):  
Arroyyan Ramly
Keyword(s):  
Time Series Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Secondary Data ◽  
Primary Data ◽  
Series Data ◽  
P Value ◽  
Analysis Method ◽  
Effect Model ◽  
The Village

This study aims to analyze and see the effectiveness of the distribution of the use of village funds in Kuala Subdistrict, Nagan Raya Regency and its relationship with poverty levels. The data used is in the form of time series data from 2015 to 2018 which is collected through primary and secondary data. Primary data were obtained by directly visiting villages in the Kuala sub-district. Meanwhile, secondary data were obtained from the website of the Central Statistics Agency (BPS), document review, articles related to the object of research. This study conducted observations of 10 villages as a sample of 17 villages in Kuala District. The analysis method uses panel data regression with the random effect model (REM) analysis method. From the regression results of the random effect model, it was found that the village fund variable had a positive and significant effect on poverty with a probability of 0.0000 = p-value α = 5%. Then the village fund allocation variable has a significant negative effect on poverty with a probability of 0.0000 = p-value α = 5%. This means that adding 1% of village funds or increasing village funds will reduce poverty in Kuala Subdistrict, Nagan Raya Regency.

Pengaruh Variabel Ekonomi Makro terhadap Yield Spread Negara-negara di Asia Timur, Amerika Latin, dan Karibian

2018 ◽  
Vol 3 (1) ◽  
pp. 61-67
Author(s):  
Irwan Diko Purba
Keyword(s):  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Least Square ◽  
Real Effective Exchange Rate ◽  
Yield Spread ◽  
Macroeconomic Variables ◽  
Effect Model ◽  
Effective Exchange ◽  
Effective Exchange Rate

A country’s credit worthiness decided by macroeconomic factors. This research aims to examine the impact of macroeconomic and external factor on yield spread of East Asia, Latin America, and Caribbean countries. Macroeconomic variables used in this research are classified as macroeconomic variables that influence liquidity and solvency, and macroeconomic variables that influence macroeconomic fundamental. This research is conducted by using quarterly yield spread data of 11 countries from 2000Q1 to 2015Q4 and analyzed panel data regression using Pooled Least Square (PLS), Fixed Effect Model (FEM) and Random Effect Model (REM). Study results show that macroeconomic variables that have impact on yield spread are external debt to GDP ratio, fiscal balance to GDP ratio, amortization to international reserve ratio, current account to GDP ratio, real effective exchange rate, and GDP per capita growth. External factors that have impact on yield spread are US Treasury Bond 10 year yield and Volatility Index. Abstrak Kelayakan utang (credit worthiness) sebuah negara ditentukan dari kondisi ekonomi makro negara tersebut dan faktor eksternal. Penelitian ini bertujuan untuk menguji pengaruh faktor ekonomi makro serta faktor eksternal terhadap yield spread negara-negara di Asia Timur, Amerika Latin dan Karibian.  Variabel ekonomi makro yang digunakan dalam penelitian ini digolongkan dalam dua kelompok yakni yang memengaruhi likuiditas dan solvensi serta yang memengaruhi fundamental ekonomi makro. Penelitian dilakukan dengan menggunakan yield spread triwulanan dari 11 negara untuk periode 2000Q1:2015Q4 dan analisis regresi data panel menggunakan Pooled Least Square (PLS), Fixed Effect Model (FEM) dan Random Effect Model (REM). Hasil penelitian menunjukkan bahwa variabel ekonomi makro yang memengaruhi yield spread adalah rasio utang luar negeri terhadap PDB,  rasio keseimbangan anggaran fiskal terhadap PDB, rasio amortisasi terhadap cadangan devisa, rasio transaksi berjalan terhadap PDB, nilai tukar riil (real effective exchange rate) dan  pertumbuhan PDB per kapita. Faktor eksternal yang memengaruhi yield spread adalah yield US Treasury 10 tahun dan Volatility Index (VIX).

scholarly journals MODEL REGRESI DATA PANEL UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DI PULAU MADURA

2020 ◽  
Vol 9 (3) ◽  
pp. 355-363
Author(s):  
Artanti Indrasetianingsih ◽  
Tutik Khalimatul Wasik
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Poverty Level ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Poverty arises when a person or group of people is unable to meet the level of economic prosperity which is considered a minimum requirement of a certain standard of living or poverty is understood as a state of lack of money and goods to ensure survival. Panel data regression is the development of regression analysis which is a combination of time series data and cross section data. Panel data regression is usually used to make observations of data that is examined continuously for several periods. The purpose of this study is to determine the factors that influence the level of poverty in Madura Island in the period 2008 - 2017. In this study the variables used in this study are life expectancy (X1), average length of school (X2), level open unemployment (X3), and labor force participation (X4) with the Comman Effect Model (CEM) approach, Fixed Effect Model and Random Effect Model (REM). To choose the best model from the three is the chow test, the hausman test and the breusch-pagan test. In this study, the best model chosen was the Fixed Effect Model. Keywords: CEM, Fixed Effect Model, Data Panel Regression, REM, Poverty level.

scholarly journals Determinant of Islamic Corporate Social Responsibility (ICSR) Disclosure in Companies Registered in Jakarta Islamic Index (JII)

2021 ◽  
Vol 1 (02) ◽  
pp. 105-115
Author(s):  
Ainul Fatha Isman ◽  
Nur Cholifatul Aeni
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Board Of Directors ◽  
Time Series Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Influential Factor ◽  
Series Data ◽  
Effect Model ◽  
Corporate Social

Social aspects are important aspects that must be considered by every individual. Similarly, companies that must disclose social responsibility or what is called Corporate Social Responsibility (CSR) through the mandate of the Act. This research aims to determine the factors that affect the Islamic corporate social responsibility disclosure in Indonesia. This research is categorized in quantitative research with associative approaches. The population in this study amounted to 30 companies registered in the Jakarta Islamic Index in 2016-2018 with a purposive sampling technique to obtain 51 samples. The data analysis model in this study is a panel data linear regression test with a combination of time series data and cross-section which is carried out through three approaches, namely the common effect model, the fixed-effect model, and the random effect model. The results of this study indicate that partially and simultaneously the size of the company's profitability, and the Muslim board of directors influence the disclosure of the company's ICSR. The most influential factor in company ICSR disclosure is company size. The results of this study imply that each company uses the ISR Index as a reference for the preparation of sharia corporate social responsibility reporting standards and increases the number of Muslim board of directors who are competent and have innovative ideas to increase company assets, thus positively impacting ICSR disclosure.

scholarly journals Pengaruh PDRB, UMK, Jumlah Penduduk dan Inflasi Terhadap Jumlah Pengangguran di 35 Kabupaten/Kota di Jawa Tengah Tahun 2011-2017

2020 ◽  
Vol 5 (1) ◽  
pp. 66-76
Author(s):  
Niken Dwi Lestari ◽  
Nenik Woyanti
Keyword(s):  
Panel Data ◽  
Minimum Wage ◽  
Cross Section ◽  
Time Series Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Secondary Data ◽  
Variable Number ◽  
Series Data ◽  
Effect Model

Economic growth can be used as a benchmark of the success of a region’s economic development. This study aims to analyze the effect of GRDP, minimum wage districs / cities, population and inflation variables on the number of unemployment in 35 districs / cities in Central Java Province in 2011-2017.The method which is used in this study is the panel data method, that is cross section data as many as 35 districts / cities and 7 years time series data with Random Effect Model (REM) approach. The type of data used is secondary data and data processing tools used are Eviews 9. The results of this study indicate that the gross regional domestic product (GRDP) variable has a positive and significant effect on the number of unemployment. The minimum wage districs / cities variable has a negative and significant effect on the number of unemployment. The variable number of population has a positive and significant effect on the number of unemployment. The inflation variable has a positive and insignificant effect on the number of unemployment.Keywords : Number Of Unemployment; GRDP; Minimum Wage Districts / Cities; Population; InflationPertumbuhan ekonomi merupakan suatu tolok ukur keberhasilan pembangunan ekonomi suatu daerah. Penelitian ini bertujuan untuk menganalisis pengaruh variabel PDRB, upah minimum kabupaten/kota, jumlah penduduk dan inflasi tehadap jumlah pengangguran di 35 Kabupaten/Kota di Provinsi Jawa Tengah tahun 2011-2017. Metode yang digunakan dalam penelitian ini adalah metode panel data yaitu data cross section sebanyak 35 kabupaten/kota dan data runtut waktu 7 tahun dengan pendekatan Random Effect Model (REM). Jenis data yang digunakan adalah data sekunder dan alat pengolah data yang digunakan adalah Eviews 9. Hasil dari penelitian ini menunjukkan bahwa variabel produk domestik regional bruto (PDRB) memiliki pengaruh positif dan signifikan terhadap jumlah pengangguran. Variabel upah minimum kabupaten/kota memiliki pengaruh negatif dan signifikan terhadap jumlah pengangguran. Variabel jumlah penduduk memiliki pengaruh positif dan signifikan terhadap jumlah pengangguran. Variabel inflasi memiliki pengaruh positif dan tidak signifikan terhdap jumlah pengangguran.Kata Kunci: Jumlah Pengangguran; PDRB; Upah Minimum Kabupaten / Kota; Jumlah Penduduk; Inflasi

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