Minimax A‐, c‐, and I‐optimal regression designs for models with heteroscedastic errors

2021 ◽  
Author(s):  
Hanan Abousaleh ◽  
Julie Zhou
Keyword(s):  
Heteroscedastic Errors

scholarly journals Estimation and inference of semi-varying coefficient models with heteroscedastic errors

2014 ◽  
Vol 124 ◽  
pp. 70-93 ◽  
Author(s):  
Si-Lian Shen ◽  
Jian-Ling Cui ◽  
Chang-Lin Mei ◽  
Chun-Wei Wang
Keyword(s):  
Varying Coefficient Models ◽  
Varying Coefficient ◽  
Heteroscedastic Errors

Testing conditional moment restriction models using empirical likelihood

2021 ◽  
Author(s):  
Yves G Berger
Keyword(s):  
Empirical Likelihood ◽  
Model Specification ◽  
Ratio Test ◽  
Conditional Moment ◽  
Nominal Level ◽  
Empirical Likelihood Ratio Test ◽  
Likelihood Model ◽  
Heteroscedastic Errors ◽  
Endogenous Covariates ◽  
The Relationship

Abstract An empirical likelihood test is proposed for parameters of models defined by conditional moment restrictions, such as models with non-linear endogenous covariates, with or without heteroscedastic errors or non-separable transformation models. The number of empirical likelihood constraints is given by the size of the parameter, unlike alternative semi-parametric approaches. We show that the empirical likelihood ratio test is asymptotically pivotal, without explicit studentisation. A simulation study shows that the observed size is close to the nominal level, unlike alternative empirical likelihood approaches. It also offers a major advantages over two-stage least-squares, because the relationship between the endogenous and instrumental variables does not need to be known. An empirical likelihood model specification test is also proposed.

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