Empirical likelihood based diagnostics for heteroscedasticity in partially linear errors-in-variables models |
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| Authors: | Heung Wong Feng Liu Min Chen Wai Cheung Ip |
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| Affiliation: | 1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong;2. Department of Statistics, Chongqing Institute of Technology, Chongqing 400050, China;3. Academy of Mathematics and Systems Science, CAS, Beijing 100080, China |
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| Abstract: | A standard assumption in regression analysis is homogeneity of the error variance. Violation of this assumption can have adverse consequences for the efficiency of estimators. In this paper, we propose an empirical likelihood based diagnostic technique for heteroscedasticity in the partially linear errors-in-variables models. Under mild conditions, a nonparametric version of Wilk's theorem is derived. Simulation results reveal that our test performs well in both size and power. |
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| Keywords: | Heteroscedasticity Empirical likelihood ratio Partially linear models Errors-in-variables Nuisance parameter |
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