scholarly journals Robust Bayesian seemingly unrelated regression model

2018 ◽  
Vol 34 (3) ◽  
pp. 1135-1157
Author(s):  
Chamberlain Mbah ◽  
Kris Peremans ◽  
Stefan Van Aelst ◽  
Dries F. Benoit
Keyword(s):  
Regression Model ◽  
Seemingly Unrelated Regression ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model

Is there a Leviathan at the Border?

2001 ◽  
Vol 19 (2) ◽  
pp. 123-141
Author(s):  
Marvin E. Dodson
Keyword(s):  
Labor Market ◽  
Theoretical Analysis ◽  
Regression Model ◽  
Host Country ◽  
Seemingly Unrelated Regression ◽  
Labor Market Conditions ◽  
Skilled Immigrants ◽  
Demand Functions ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model

Abstract This article provides a theoretical analysis of host country immigrant demand using the Leviathan model of government. The analysis considers both unskilled and skilled immigrants. A seemingly unrelated regression model tests the implications of the resulting demand functions. The approach in this model incorporates labor market indicators unlike the limited previous work in this area. Possible non-pecuniary benefits of immigration and numerical limitations on immigrant admissions are also included as factors in the model. Results of the specification suggest that labor market conditions and non-pecuniary benefits do impact the demand for immigrants. Furthermore, the results show that a total limit on immigration will increase the skill level of the host country.

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.

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