Robust Estimation and Inference for Threshold Models with Integrated Regressors
Author : Haiqiang Chen
Publisher :
Page : pages
File Size : 28,12 MB
Release : 2013
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ISBN :
Author : Haiqiang Chen
Publisher :
Page : pages
File Size : 28,12 MB
Release : 2013
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Author :
Publisher :
Page : pages
File Size : 43,1 MB
Release : 2017
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Author : Pavel Cizek
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Page : 0 pages
File Size : 22,33 MB
Release : 2009
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ISBN :
The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can be used in place of least squares, these robust estimators cannot be easily applied to models containing binary and categorical explanatory variables. Therefore, I design a robust estimator that can be used for any linear regression model no matter what kind of explanatory variables the model contains. Additionally, I propose an adaptive procedure that maximizes the efficiency of the proposed estimator for a given data set while preserving its robustness.
Author : Stanford University. Econometric Workshop
Publisher :
Page : 48 pages
File Size : 18,37 MB
Release : 1985
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Author : Paul Čižek
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Page : pages
File Size : 15,47 MB
Release : 2007
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Author : Eva Cantoni
Publisher :
Page : 22 pages
File Size : 44,58 MB
Release : 1999
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Author : Eva Cantoni
Publisher :
Page : 44 pages
File Size : 34,59 MB
Release : 1999
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Author : Stanford University. Econometric Workshop
Publisher :
Page : 61 pages
File Size : 25,30 MB
Release : 1986
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Author : Pavel Čížek
Publisher :
Page : 86 pages
File Size : 19,55 MB
Release : 2001
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ISBN : 9788086288666
Author : Hai-Li Hsiang Wang
Publisher :
Page : 288 pages
File Size : 44,56 MB
Release : 1979
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ISBN :