Generalized canonical correlation variables improved estimation in high dimensional seemingly unrelated regression models

2017 ◽  
Vol 126 ◽  
pp. 119-126 ◽  
Author(s):  
Li Zhao ◽  
Xingzhong Xu
Keyword(s):  
Canonical Correlation ◽  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
High Dimensional ◽  
Unrelated Regression

scholarly journals Estimation of high-dimensional seemingly unrelated regression models

2021 ◽  
Vol 40 (9) ◽  
pp. 830-851
Author(s):  
Lidan Tan ◽  
Khai Xiang Chiong ◽  
Hyungsik Roger Moon
Keyword(s):  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
High Dimensional ◽  
Unrelated Regression

scholarly journals Building W Matrices Using Selected Geostatistical Tools: Empirical Examination and Application

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Elżbieta Antczak
Keyword(s):  
Spatial Data ◽  
Spatial Dependence ◽  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
Spatial Data Analysis ◽  
Spatial Dependency ◽  
Empirical Examination ◽  
Spatial Panel ◽  
Spatial Weights ◽  
Unrelated Regression

This paper investigates how to determine the values (elements) of spatial weights in a spatial matrix (W) endogenously from the data. To achieve this goal, geostatistical tools (standard deviation ellipsis, semivariograms, semivariogram clouds, and surface trend models) were used. Then, in the econometric part of the analysis, the effect of applying different variants of matrices was examined. The study was conducted on a sample of 279 Polish towns from 2005–2015. Variables were related to the quantity of produced waste and economic development. Both exploratory spatial data analysis and estimations of spatial panel and seemingly unrelated regression models were performed by including particular W matrices in the study (exogenous-random as well as distance and directional matrices constructed based on data). The results indicated that (1) geostatistical tools can be effectively used to build Ws; (2) outcomes of applying different matrices did not exclude but supplemented one another, although the differences were significant; (3) the most precise picture of spatial dependence was achieved by including distance matrices; and (4) the values of the assessed parameter at the regressors did not significantly change, although there was a change in the strength of the spatial dependency.

Two-parameter ridge estimation in seemingly unrelated regression models

2020 ◽  
pp. 1-15
Author(s):  
Robab Mehdizadeh Esfanjani ◽  
Dariush Najarzadeh ◽  
Hossein Jabbari Khamnei ◽  
Farshin Hormozinejad ◽  
Mahnaz Talebi
Keyword(s):  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
Ridge Estimation ◽  
Unrelated Regression ◽  
Two Parameter
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