Conditional variance estimation in heteroscedastic regression models
| Autor: | Liang Peng, Ming-Yen Cheng, Lu-Hung Chen |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Statistics and Probability
Heteroscedasticity Applied Mathematics Autoregressive conditional heteroskedasticity Conditional probability distribution Law of total variance One-way analysis of variance Statistics Econometrics Statistics Probability and Uncertainty Variance-based sensitivity analysis Conditional variance Mathematics Variance function |
| Zdroj: | Journal of Statistical Planning and Inference. 139:236-245 |
| ISSN: | 0378-3758 |
| DOI: | 10.1016/j.jspi.2008.04.020 |
| Popis: | First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique to improve the inference for the conditional variance. The proposed methods are investigated through their asymptotic distributions and numerical performances. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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