A consistent test for heteroscedasticity in semi-parametric regression with nonparametric variance function based on the kernel method
| Autor: | Jin-Guan Lin, Xiao-Yi Qu |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
Statistics::Theory Heteroscedasticity Statistics::Applications Nonparametric regression Statistics::Machine Learning Homoscedasticity Statistics Econometrics Statistics::Methodology Kernel regression Semiparametric regression Statistics Probability and Uncertainty Park test Goldfeld–Quandt test Mathematics Variance function |
| Zdroj: | Statistics. 46:565-576 |
| ISSN: | 1029-4910 0233-1888 |
| DOI: | 10.1080/02331888.2010.543464 |
| Popis: | It is important to detect the variance heterogeneity in regression models. Heteroscedasticity tests have been well studied in parametric and nonparametric regression models. This paper presents a consistent test for heteroscedasticity for nonlinear semi-parametric regression models with nonparametric variance function based on the kernel method. The properties of the test are investigated through Monte Carlo simulations. The test methods are illustrated with a real example. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|