scholarly journals Panel Regression Models for Paddy (Oryza sativa) Crop Production

YMER Digital ◽  
2022 ◽  
Vol 21 (01) ◽  
pp. 28-40
Author(s):  
Rajarathinam A ◽  
◽  
Suba S S ◽  
Keyword(s):  
Regression Model ◽  
Tamil Nadu ◽  
Crop Production ◽  
Wald Test ◽  
Fixed Effect Model ◽  
Panel Regression ◽  
Effect Model ◽  
Tamil Nadu State ◽  
Crop Area ◽  
Paddy Crop

The present investigation was carried out to study area production trends of Paddy crop grown in different districts of Tamil Nadu state, India during the period 1998-99 to 2010- 2020 based on Panel Regression Model. The statistically most suited Panel Regression model was selected based on Hausman and Wald test. The study variables namely the area under the Paddy crop (AREA) and the production (PRODN) of Paddy crop were found to be stationary at level. Analysis of variance test indicated that district to district crop productions were highly significant. Highest area under the crops and productions were registered in Tiruvarur, Thanjavur etc., Very lowest were registered in Coimbatore and Nilgiris districts. The fixed effect model was found to be suitable to study the trend and this model explains the 87% of variations in Paddy crop production.

scholarly journals Modeling and Monitoring COVID-19 Monthly Infected Cases and Deaths

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM
Keyword(s):  
Regression Model ◽  
Tamil Nadu ◽  
Panel Regression ◽  
The Novel ◽  
Economic Crises ◽  
The World ◽  
Tamil Nadu State ◽  
Novel Coronavirus

Abstract The effects of the novel coronavirus (COVID-19) pandemic could not have been more profound, with the world encountering health crises as well as enormous economic crises. In this paper, the relationships, and trends in the number of COVID-19 infected new cases and the number of deaths due to COVID-19 in all 37 districts of Tamil Nadu state, India, during the period, 3rd July 2020 to 31st March,2021 based on a panel regression model.

scholarly journals Panel Data Modelling of COVID-19 Infected Cases

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
TAMILSELVAN PAKKIRISAMY
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Tamil Nadu ◽  
Wald Test ◽  
Infected Case ◽  
Panel Data Regression ◽  
Data Regression ◽  
Tamil Nadu State ◽  
The Government ◽  
Novel Coronavirus

Abstract Background and Objective: The novel coronavirus pandemic, known as COVID-19, could not have been more predictable; thus, the world encountered health crises and substantial economic crises. This paper analyses the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins published through the official website www.stopcorona.tn.gov.in maintained by the Government of Tamil Nadu state, India. Panel data regression models were employed to study the trends. EViews Ver.11. Software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanniyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. The fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanniyakumari districts. A random e2ffects model was found to be an appropriate model to study the trend.Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October 2020.

scholarly journals Panel Data Modelling of COVID-19 Infected Cases

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
TAMILSELVAN PAKKIRISAMY
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Tamil Nadu ◽  
Wald Test ◽  
Infected Case ◽  
Panel Data Regression ◽  
Data Regression ◽  
Tamil Nadu State ◽  
The Government ◽  
Novel Coronavirus

Abstract Background and Objective: The novel coronavirus pandemic, known as COVID-19, could not have been more predictable; thus, the world encountered health crises and substantial economic crises. This paper analyses the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins published through the official website www.stopcorona.tn.gov.in maintained by the Government of Tamil Nadu state, India. Panel data regression models were employed to study the trends. EViews Ver.11. Software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanniyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. The fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanniyakumari districts. A random e2ffects model was found to be an appropriate model to study the trend. Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October 2020.

scholarly journals The Step Construction of Geographically Weighted Panel Regression in Air Polluter Standard Index (APSI) Data

2018 ◽  
Vol 73 ◽  
pp. 12006
Author(s):  
Budi Warsito ◽  
Hasbi Yasin ◽  
Dwi Ispriyanti ◽  
Arief Rachman Hakim
Keyword(s):  
Regression Model ◽  
Weighting Function ◽  
Weighted Least Squares ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Local Linear Regression ◽  
Coefficient Of Determination ◽  
Panel Regression ◽  
Standard Index ◽  
Effect Model

Geographically Weighted Panel Regression or GWPR is a local linear regression model that combines GWR model and panel data regression model with considering spatial effect, especially spatial heterogeneity problem. This article is focused on the soft computation of GWPR model using Fixed Effect Model (FEM). Parameter estimation in GWPR is obtain by Weighted Least Squares (WLS) methods and the resulting model for each location will be different from one to another. This study will compare the fixed-effect GWPR model with several weighting functions. The best model is determined based on the biggest coefficient of determination (R2) value. In this study, the model is applied in the Air Polluter Standard Index (APSI) in Surabaya City, East Java. The results of this study indicate that Fixed Effect GWPR model with a fixed exponential kernel weighting function is the best model to describe the APSI because it has the smallest AIC.

scholarly journals Panel Data Modelling for Indian Food Grain Production

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
Subh S S ◽  
Ramji Madhaiyan
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Uttar Pradesh ◽  
Fixed Effect Model ◽  
Panel Regression ◽  
Data Modelling ◽  
Grain Production ◽  
Effect Model ◽  
Food Grain ◽  
Indian Food

Abstract The present investigation was carried out to study the food grain production trends in different states in India based on Panel Regression Model for the period 2001-02 to 2020-2021. The results reveal that between state-to-state food grain production is highly significant the highest food grain production was registered in Uttar Pradesh followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. The findings reveal that the highly significant fixed effect model was found to be suitable to study the trend and this model explains the 82% of variations in food grain production. Over all increasing in food grain production is noted.

scholarly journals Analisis Regresi Panel pada Pemodelan Tingkat Kematian Bayi di Provinsi Sulawesi Selatan

2020 ◽  
Vol 2 (2) ◽  
pp. 115
Author(s):  
Syafruddin Side ◽  
S. Sukarna ◽  
Raihana Nurfitrah
Keyword(s):  
Panel Data ◽  
Infant Mortality ◽  
Regression Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Dummy Variable ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Penelitian ini membahas mengenai estimasi parameter model regresi data panel pada pemodelan tingkat kematian bayi di Provinsi Sulawesi Selatan dari tahun 2014 sampai dengan 2015. Data yang digunakan adalah data sekunder dari Dinas Kesehatan Provinsi Sulawesi Selatan yang berupa jumlah kematian bayi, berat bayi lahir rendah, persalinan yang ditolong oleh tenaga kesehatan, penduduk miskin, bayi yang diberi ASI ekslusif dan rumah tangga berperilaku bersih sehat di seluruh Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2014-2016. Analisis data dilakukan dengan menggunakan penghitungan manual dan dengan menggunakan software EViews 9. Pembahasan dimulai dari melakukan estimasi parameter model regresi data panel, menentukan model regresi data panel terbaik, , menguji asumsi model regresi data panel, pengujian signifikansi parameter dan interpretasi model regresi. Dalam penelitian ini diperoleh kesimpulan yaitu estimasi model regresi data panel terbaik dengan pendekatan fixed effect model.Kata kunci:Regresi Data Panel, Kematian Bayi, Fixed Effect Model, Least Square Dummy Variable. This research discusses about parameter estimation of panel data regression model of infant mortality level modelling in South Sulawesi from 2014 to 2015. The data used were secondary data from Dinas Kesehatan Provinsi Sulawesi Selatan in the form of number of infant mortality, low weight of infant, childbirth rescued by health workers, poor population, infants who were given exclusive breast milk and household that behaves well in the whole district/town in South Sulawesi year 2014-2016. Data analysis was performed using the calculation manually and by using EViews 9 software. The discussion started from doing parameter estimation of panel data regression model, determining the best panel data regression model, testing the assumption of panel data regression model, testing the signification of parameter and interpretation of regression model. Conclusion of this research are the estimation of regression model is the best panel data regression model with fixed effects model approach.Keywords:Panel Data Regression, Infant Mortality, Fixed Effect Model, Least Square Dummy Variable.

scholarly journals Do Independent Commissioners Control the Effect of Family-Owned Business Characteristics on Dividend Policy? A Study in Indonesian Manufacturing Companies

2021 ◽  
Vol 2 (2) ◽  
pp. 55-62
Author(s):  
Ernie HENDRAWATY ◽  
Sri HASNAWATI ◽  
Lia PURNAMASARI
Keyword(s):  
Panel Data ◽  
Family Business ◽  
Dividend Policy ◽  
Fixed Effect Model ◽  
Financial Data ◽  
Panel Regression ◽  
Manufacturing Companies ◽  
Effect Model ◽  
Positive Effect

This study aims to determine the role of independent Commissioners to control the effect of family-owned business characteristics on dividend policy. This study construct panel data that estimate using panel regression with a fixed-effect model. The model is estimated using financial data of 64 Indonesian manufacturing companies that were observed from the period 2016-2018. The result showed that family-owned business characteristics have a positive effect on dividens. The Independent Commisioners were able to control the effect of family business characteristics on the dividend policy. The Independent Commissioners have a role in reducing the positive effect of family-owned businesses characteristics on dividends.

scholarly journals Estimating the Effect of Climatic Variables and Crop Area on Maize Yield in Ghana

2012 ◽  
Vol 3 (9) ◽  
pp. 313-321
Author(s):  
Henry De-Graft Acquah
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Crop Yield ◽  
Crop Production ◽  
Growing Season ◽  
Maize Yield ◽  
Climatic Variables ◽  
Non Linear ◽  
Crop Area

Climate change tends to have negative effects on crop yield through its influence on crop production. Understanding the relationship between climatic variables, crop area and crop yield will facilitate development of appropriate policies to cope with climate change. This study therefore examines the effects of climatic variables and crop area on maize yield in Ghana based on regression model using historical data (1970-2010). Linear and Non-linear regression model specifications of the production function were employed in the study. The study found that growing season temperature trend is significantly increasing by 0.03oC yearly whereas growing season rainfall trend is insignificantly increasing by 0.25mm on yearly basis. It was also observed that rainfall is becoming increasingly unpredictable with poor distributions throughout the season. Results from the linear and non-linear regression models suggest that rainfall increase and crop area expansion have a positive and significant influence on mean maize yield. However, temperature increase will adversely affect mean maize yield. In conclusion, the study found that there exists not only a linear but also a non-linear relationship between climatic variables and maize yield.

scholarly journals Effect of Climate Change on Maize Productivity: Panel Data Analysis

2020 ◽  
pp. 41-45
Author(s):  
Parthasarathi Gurusamy ◽  
Balasubramanian Rudrasamy
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Climate Variability ◽  
Tamil Nadu ◽  
Crop Production ◽  
Panel Data Analysis ◽  
Panel Regression ◽  
Maize Crop ◽  
North East ◽  
The Impact

Aim: The maize is widely grown all parts of the world and it is consumed by all people. This paper studies the impact of climate variability on yield of maize crop in Tamil Nadu using Panel regression analysis.      Study Design: Rainfall (max and min), Temperature (max and min) and yield details were collected from the Indian Meteorological Department and crop production reports respectively used for analysis.  Place and Duration: Tamil Nadu, India. Methodology: Panel data model was used to estimate crop production functions. Results and Conclusion: The study focused on the impact of climate variability on yield of maize crop in Tamil Nadu using Panel regression analysis. The high rainfall leads to The effect of NEM rainfall on maize yield is dependent on the level of NEM temperature and vice-versa. This is probably because of the fact that in most of the districts in Tamil Nadu, maize is grown as a rainfed crop in north-east monsoon season with lower temperature and hence increase in temperature together with good amount of rainfall would lead to higher yield of maize.

Sign in / Sign up

Export Citation Format

Share Document