Kernel estimation of hazard functions when observations have dependent and common covariates
We propose a hazard model where dependence between events is achieved by assuming dependence between covariates. This model allows for correlated variables specific to observations as well as macro variables which all observations share. This setup better fits many economic and financial application...
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| Format: | Working paper |
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University of Oxford
2015
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| author | Wolter, J |
| author_facet | Wolter, J |
| author_sort | Wolter, J |
| collection | OXFORD |
| description | We propose a hazard model where dependence between events is achieved by assuming dependence between covariates. This model allows for correlated variables specific to observations as well as macro variables which all observations share. This setup better fits many economic and financial applications where events are not independent. Nonparametric estimation of the hazard function is then studied. Kernel estimators proposed in Nielsen and Linton (1995, Annals of Statistics) and Linton, Nielsen and Van de Geer (2003, Annalsof Statistics) are shown to have similar asymptotic properties compared with the i.i.d.case. Mixing conditions ensure the asymptotic results follow. These results depend on adjustments to bandwidth conditions. Simulations are conducted which verify the impact of dependenceon estimators. Bandwidth selection accounting for dependence is shown to improve performance. In an empirical application, trade intensity in high-frequency financial data is estimated. |
| first_indexed | 2024-03-07T04:25:08Z |
| format | Working paper |
| id | oxford-uuid:cc5a232f-7d6d-4b21-b071-f937ef6d7ee1 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:25:08Z |
| publishDate | 2015 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:cc5a232f-7d6d-4b21-b071-f937ef6d7ee12022-03-27T07:21:21ZKernel estimation of hazard functions when observations have dependent and common covariatesWorking paperhttp://purl.org/coar/resource_type/c_8042uuid:cc5a232f-7d6d-4b21-b071-f937ef6d7ee1Bulk import via SwordSymplectic ElementsUniversity of Oxford2015Wolter, JWe propose a hazard model where dependence between events is achieved by assuming dependence between covariates. This model allows for correlated variables specific to observations as well as macro variables which all observations share. This setup better fits many economic and financial applications where events are not independent. Nonparametric estimation of the hazard function is then studied. Kernel estimators proposed in Nielsen and Linton (1995, Annals of Statistics) and Linton, Nielsen and Van de Geer (2003, Annalsof Statistics) are shown to have similar asymptotic properties compared with the i.i.d.case. Mixing conditions ensure the asymptotic results follow. These results depend on adjustments to bandwidth conditions. Simulations are conducted which verify the impact of dependenceon estimators. Bandwidth selection accounting for dependence is shown to improve performance. In an empirical application, trade intensity in high-frequency financial data is estimated. |
| spellingShingle | Wolter, J Kernel estimation of hazard functions when observations have dependent and common covariates |
| title | Kernel estimation of hazard functions when observations have dependent and common covariates |
| title_full | Kernel estimation of hazard functions when observations have dependent and common covariates |
| title_fullStr | Kernel estimation of hazard functions when observations have dependent and common covariates |
| title_full_unstemmed | Kernel estimation of hazard functions when observations have dependent and common covariates |
| title_short | Kernel estimation of hazard functions when observations have dependent and common covariates |
| title_sort | kernel estimation of hazard functions when observations have dependent and common covariates |
| work_keys_str_mv | AT wolterj kernelestimationofhazardfunctionswhenobservationshavedependentandcommoncovariates |