Computation of standard errors in seemingly unrelated regression equation models

1986 ◽  
Vol 15 (12) ◽  
pp. 3583-3588
Author(s):  
Anil K. Srivastava ◽  
Derrick S. Tracy
Keyword(s):  
Regression Equation ◽  
Seemingly Unrelated Regression ◽  
Standard Errors ◽  
Unrelated Regression

Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations

1996 ◽  
Vol 12 (3) ◽  
pp. 569-580 ◽  
Author(s):  
Paul Rilstone ◽  
Michael Veall
Keyword(s):  
Monte Carlo ◽  
Regression Model ◽  
Confidence Intervals ◽  
Seemingly Unrelated Regression ◽  
Regression Coefficients ◽  
Standard Errors ◽  
Regression Equations ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Downward Bias

The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors.

A Note on the Efficiency of Seemingly Unrelated Regression

1988 ◽  
Vol 42 (2) ◽  
pp. 137-139 ◽  
Author(s):  
James K. Binkley ◽  
Carl H. Nelson
Keyword(s):  
Seemingly Unrelated Regression ◽  
Unrelated Regression

Workplace disability and job satisfaction in Britain: A co-worker test?

2021 ◽  
pp. 0143831X2110142
Author(s):  
Getinet Astatike Haile
Keyword(s):  
Job Satisfaction ◽  
Negative Relationship ◽  
Organisational Culture ◽  
Seemingly Unrelated Regression ◽  
Unrelated Regression ◽  
Significant Negative Relationship ◽  
Using Data

The article examines the link between workplace disability (WD) and workplace job satisfaction (JS) using data from WERS2011. Controlling for a rich set of workplace characteristics including organisational culture, the study finds a significant negative relationship between JS and the share of disabled respondents within workplaces. Notably, Seemingly Unrelated Regression (SUR)-based analysis distinguishing between disabled and non-disabled respondents reveals that the negative relationship found is specific to non-disabled respondents. Moreover, disability equality policies are found to be significantly positively related with disabled respondents’ JS while they are negatively related with the JS of their non-disabled counterparts. The article ponders if there is a co-worker aspect to the WD–JS link and whether HR policies may need to take heed of co-worker dynamics in this respect.

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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