Effects of Family and Household's Economic Factors on Depression: Panel Regression Analysis Using Fixed-Effect Model

2013 ◽  
Vol 24 (2) ◽  
Author(s):  
Jun-Mo Sung
Keyword(s):  
Regression Analysis ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Economic Factors ◽  
Panel Regression ◽  
Effect Model

The impact of the China–USA trade war on USA Chapter 12 farm bankruptcies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yu Wu ◽  
Calum G. Turvey
Keyword(s):  
Fixed Effect Model ◽  
Mixed Effects ◽  
Cubic Splines ◽  
Fixed Effect ◽  
Economic Factors ◽  
Producer Price ◽  
Content Type ◽  
Effect Model ◽  
Trade War ◽  
The Impact

PurposeThe purpose of this paper is to determine the effects of the 2018–2020 China–US trade war on US farm bankruptcies as filed under Chapter 12. The key task is to identify the economic factors affecting farm bankruptcies generally, and to then control for the trade war impacts including the Market Facilitation Program (MFP), floods, agricultural conditions and the health of agricultural finance leading into the trade war.Design/methodology/approachResults were obtained using ordinary least square regression and panel fixed effect model using bankruptcy rates and number as the dependent variable. Independent variables included market effects, credit conditions, yield variation, trade impacts, 2019 flooding, macroeconomic conditions and regional fixed effects. The authors use cubic splines to interpolate annual and quarterly data to a monthly base.FindingsBased on a fixed effect model, the authors find that all other things being equal the China–USA trade war would have had a significant impact on Chapter 12 farm bankruptcies, increasing the bankruptcy rate by 25.7%. The flooding in 2009 had minor effects of increasing the rate by only 0.05%. The overall impact will, however be substantially lower than the 25.7% because of the MFP. The MFP variables (binary) had mixed effects and its true impact is unknowable at this time; however, the authors also find that a 1% increase in the producer price index decreases bankruptcy rates by 2.62% and farm bankruptcy numbers by 3.70%. Likewise a 1% increase in GDP reduces bankruptcies by 3.25%. These suggest that the MFP program will have likely reduced farm bankruptcies considerably than what would have occurred in their absence. The authors also find that states heavily dependent on trade faced lower market uncertainty. Broader economic factors (net charge-offs of farm loans held by insured commercial banks, US real GDP, the average effective interest rate on nonreal estate farm loans) affect farm bankruptcy.Research limitations/implicationsThe authors use monthly bankruptcy statistics, however not all data were available in monthly measures requiring interpolation using cubic spline functions to approximate monthly changes in some variables. Although the MFP had mixed effects in the model, the mid- to longer-term effects may be more impactful. These longer-term effects (and even shorter-term effects through 2020) are complicated by the coronavirus disease 2019 (COVID-19) pandemic, which will require a different identification strategy than that employed in this paper.Originality/valueThe analysis and results of this paper are, to the authors' knowledge, the first to investigate the impact of the China–US trade war on Chapter 12 farm bankruptcy filings. The use of cubic splines in the interpolation of agricultural data is also a technical innovation.

scholarly journals PENGARUH TINGKAT PENDIDIKAN, INVESTASI, DAN TINGKAT UPAH TERHADAP ANGKATAN KERJA YANG AKTIF BEKERJA DI INDONESIA

Jurnal Ecogen ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 691
Author(s):  
Vivi Violita Firda ◽  
Syamsul Amar
Keyword(s):  
Secondary Data ◽  
Work Force ◽  
Fixed Effect Model ◽  
Equation Model ◽  
Education Level ◽  
Fixed Effect ◽  
Panel Regression ◽  
Effect Model ◽  
Positive Effect ◽  
Level 2

This study aims to find out and analyze the influence of (1) Education Level, (2) Investment, and (3) Wage Levels on the workforce that is actively working in Indonesia by using the panel regression equation model and using the Fixed Effect Model (FEM) approach. The estimation results show that (1) Education Level has a positive and significant effect on the workforce that is actively working in Indonesia, (2) Investment has a positive and not significant effect on the workforce that is actively working in Indonesia, (3) Wage Levels have a positive and significant effect on workforce that is actively working in Indonesia.This type of research is descriptive and associative. Data type is secondary data. This study uses panel data, which uses 32 provinces in Indonesia using the Fixed Effect Model (FEM) approach.The results of this study indicate that: (1) Education Level has a positive and significant effect on the Active Work Force in Indonesia (2) Investment has a positive effect and has no significant effect on the Active Work Force in Indonesia. (3) Wage Levels have a positive and significant effect on the Active Work Force in Indonesia. Keywords: Labor Force, Education Level, Investment, and Wage Level

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 PENGARUH TINGKAT PENDIDIKAN, PENGANGGURAN , DAN KEMISKIAN TERHADAP KRIMINALITAS DI INDONESIA

2019 ◽  
Vol 1 (1) ◽  
pp. 21
Author(s):  
Suci Rahmalia ◽  
Ariusni Ariusni ◽  
Mike Triani
Keyword(s):  
Panel Data ◽  
Significant Influence ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Equation Model ◽  
Fixed Effect ◽  
Regression Equation ◽  
Panel Regression ◽  
Level Of Education ◽  
Effect Model

This study aims to determine and analyze the influence of (1) Level of Education, (2) Unemployment, and (3) Poverty against crime in Indonesia by using the panel regression equation model and using the Fixed Effect Model (FEM) approach. The estimation results show that (1) the level of education has a negative and not significant effect on criminality in Indonesia, (2) unemployment has a negative and significant effect on crime in Indonesia, (3) poverty has a positive and significant influence on crime in Indonesia.This type of research is descriptive and associative. Data type is secondary data. This study uses panel data, which uses 31 provinces in Indonesia using the Fixed Effect Model (FEM) approach.The results of this study indicate that: (1) The level of education has a negative and insignificant influence on crime in Indonesia, (2) Unemployment has a negative and significant effect on crime in Indonesia, (3) Poverty has a positive and significant influence on crime in Indonesia.

scholarly journals PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA

2021 ◽  
Vol 15 (2) ◽  
pp. 241-248
Author(s):  
Shantika Martha ◽  
Yundari Yundari ◽  
Setyo Wira Rizki ◽  
Ray Tamtama
Keyword(s):  
Agricultural Sector ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Poor People ◽  
Panel Regression ◽  
Factors Affecting ◽  
The Poor ◽  
The People ◽  
Effect Model ◽  
Adaptive Kernel

To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate. Keywords: fixed effect model, exponential adaptive kernel.

ANALISIS PENGARUH RASIO LIKUIDITAS, RASIO PROFITABILITAS, RASIO SOLVABILITAS DAN RASIO PASAR TERHADAP HARGA SAHAM PADA PERUSAHAAN YANG BERADA PADA INDEKS LQ45 DI BURSA EFEK INDONESIA

2016 ◽  
Vol 11 (02) ◽  
pp. 1-16
Author(s):  
Dilawatil Hikmah
Keyword(s):  
Fixed Effect Model ◽  
Fixed Effect ◽  
Cross Sectional ◽  
Effect Model

Penelitian ini dilakukan dengan tujuan untuk menguji pengaruh rasio likuiditas (CR), rasio profitabilitas (NPM, ROA, ROE, EPS), rasio solvabilitas (DER) dan rasio pasar (PER) terhadap harga saham (Y) pada perusahaan yang berada pada indeks LQ45 di Bursa Efek Indonesia. Metode pengumpulan data dilakukan dengan menggunakan laporan keuangan anggota emiten LQ45 periode Januari 2014 yang selama 5 tahun eksis dari Februari 2009 sampai Januari 2014. Metode sampel yang digunakan adalah purpose sampling (sampling bersyarat). Adapun jumlah sampel yang terpilih memenuhi syarat sebanyak 21 emiten dari 45 emiten. Teknis analisis data menggunakan Eviews 7.1 yaitu dengan metode cross sectional weight dengan pendekatan fixed effect model. Hasil penelitian menunjukkan bahwa secara bersama-sama maupun secara parsial variabel CR, NPM, ROA, ROE, EPS, DER, dan PER memiliki pengaruh terhadap harga saham. Namun variabel yang berpengaruh signifikan terhadap harga saham adalah NPM dan PER. Maka para investor dapat menilai kinerja perusahaan dengan melihat dari rasio keuangan dan melakukan penilaian terhadap harga saham sehingga dengan mudah dapat menentukan saham yang baik sebelum berinvestasi di BEI.

Vitamin D Receptor Gene Polymorphisms and the Risk of Metabolic Syndrome (MetS): A Meta-Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hamidreza Totonchi ◽  
Ramazan Rezaei ◽  
Shokoofe Noori ◽  
Negar Azarpira ◽  
Pooneh Mokarram ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Vitamin D ◽  
Vitamin D Receptor ◽  
Protective Factor ◽  
Meta Analysis ◽  
Receptor Gene ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Vdr Gene ◽  
Effect Model

Introduction: Several studies have assessed the association between the vitamin D receptor (VDR) polymorphism and risk of metabolic syndrome (MetS). However, the results were inconsistent and inconclusive. Therefore, we conducted a meta-analysis to clarify the exact association between the vitamin D receptor (VDR) polymorphisms and the risk of MetS. Methods: All accessible studies reporting the association between the FokI (rs2228570) or / and TaqI (rs731236) or/and BsmI (rs1544410) or/and ApaI (rs7975232 polymorphisms of the Vitamin D Receptor and susceptibility to MetS published prior to February 2019 were systematically searched in Web of Science, Scopus, and PubMed. After that, Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were estimated to evaluate the strength of the association in five genetic models. Results: A total of 9 articles based on four gene variations, and comprising 3348 participants with 1779 metabolic syndrome patients were included. The overall results suggested a significant association between BsmI (rs1544410) polymorphism and MetS susceptibility in recessive model (OR, 0.72, 95% CI, 0.55-0.95, fixed effect model), allelic model (OR, 0.83, 95% CI, 0.72-0.95, fixed effect model), and bb vs BB (OR, 0.65, 95% CI, 0.46-0.93, fixed effect). However, no significant association was identified between TaqI (rs731236) polymorphism, ApaI (rs7975232) polymorphism, and FokI (rs2228570) polymorphism and MetS. Conclusion: This meta-analysis suggested an association between the BsmI (rs1544410) polymorphism and MetS. Indeed, BsmI (rs1544410) acts as a protective factor in the MetS. As a result, the VDR gene could be regarded as a promising pharmacological and physiological target in prevention or treatment of the MetS.

Determinan Kelaparan di Indonesia Tahun 2015-2019

2021 ◽  
Vol 2021 (1) ◽  
pp. 547-556
Author(s):  
Daniel M V Mone ◽  
Efri Diah Utami
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Seemingly Unrelated Regression ◽  
Unrelated Regression ◽  
Effect Model ◽  
Development Goals

Sustainable Development Goals (SDGs) adalah sebuah perencana aksi berskala global yang disepakati oleh para pemimpin dunia, termasuk Indonesia dengan tujuan mendorong pembangunan sosial, ekonomi dan lingkungan hidup. Salah satu dari 17 tujuan SDGs adalah mengakhiri kelaparan. Berdasarkan data yang dirilis Badan Pusat Statistik, salah satu pendekatan untuk mengukur tingkat kelaparan adalah proporsi penduduk dengan asupan kalori minimum di bawah 1400 kkal/kapita/hari.  Proporsi penduduk dengan asupan kalori minimum di bawah 1400 kkal/kapita/hari di Indonesia masih cukup tinggi dan terus mengalami peningkatan dari tahun 2017 hingga 2019. Penelitian ini bertujuan untuk menganalisis bagaimana gambaran umum dari tingkat kelaparan dan variabel-variabel yang diduga mempengaruhinya, serta  bagaimana pengaruh variabel-variabel tersebut terhadap tingkat kelaparan di Indonesia tahun 2015-2019. Hasil dari penelitian ini dapat digunakan untuk merumuskan kebijakan-kebijakan guna penuntasan kelaparan di Indonesia. Metode analisis yang digunakan adalah regresi data panel dengan menggunakan  fixed effect model yang diestimasi dengan metode Seemingly Unrelated Regression (SUR). Hasil dari penelitian ini menunjukkan bahwa variabel yang berpengaruh signifikan terhadap tingkat kelaparan adalah pengeluaran makanan dan harga beras, sedangkan jumlah penduduk miskin dan pendapatan perkapita tidak berpengaruh signifikan.

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