scholarly journals Additive and Non-additive Biomass Equations for Black Wattle

2019 ◽  
Vol 26 (4) ◽  
Author(s):  
Alexandre Behling ◽  
Sylvio Péllico Netto ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Augusto Arlindo Simon ◽  
...  
Keyword(s):  
Effective Property ◽  
Seemingly Unrelated Regression ◽  
Total Biomass ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Biomass Equations ◽  
Biomass Components ◽  
Black Wattle ◽  
Undesirable Property ◽  
Second Procedure

ABSTRACT The objectives of this work were to propose additive equations for biomass components (stem and crown) and total biomass for black wattle (Acacia mearnsii De Wild.) and show the inconsistency of independently adjusted biomass equations. Two procedures were used to fit nonlinear equations of biomass: i) independent and ii) systems of equations. The second procedure, defined by the application of the seemingly unrelated regression model, has better biological and statistical properties to estimate allometric equations of biomass components and total biomass when compared with the independent estimation. An effective property of this procedure is the additivity, i.e., the estimates of component biomass are compatible with those of total biomass. Independent fitted adjusted equations do not consider the dependence between the biomass components, thus, besides the estimates being non-additive, which is an undesirable property, they will result in estimates with larger variance.

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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