Estimation of the Stochastic Conditional Duration Model via Alternative Methods.
This paper examines the estimation of the Stochastic Conditional Duration model by the empirical characteristic function and the generalized method of moments when maximum likelihood is unavailable. The joint characteristic function for the durations along with general expressions for the moments ar...
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| Format: | Journal article |
| Language: | English |
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2008
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| _version_ | 1826303805211279360 |
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| author | Knight, J Ning, C |
| author_facet | Knight, J Ning, C |
| author_sort | Knight, J |
| collection | OXFORD |
| description | This paper examines the estimation of the Stochastic Conditional Duration model by the empirical characteristic function and the generalized method of moments when maximum likelihood is unavailable. The joint characteristic function for the durations along with general expressions for the moments are derived, leading naturally to estimation via the empirical characteristic function and generalized method of moments. In a Monte Carlo study as well as an empirical application, these alternative methods are compared with quasi maximum likelihood. These experiments reveal that the empirical characteristic function approach outperforms the quasi maximum likelihood and generalized method of moments in terms of both bias and root mean square error. |
| first_indexed | 2024-03-07T06:08:15Z |
| format | Journal article |
| id | oxford-uuid:ee94f47f-eb9e-4587-9640-33ec07eda470 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:08:15Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | oxford-uuid:ee94f47f-eb9e-4587-9640-33ec07eda4702022-03-27T11:33:56ZEstimation of the Stochastic Conditional Duration Model via Alternative Methods.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ee94f47f-eb9e-4587-9640-33ec07eda470EnglishDepartment of Economics - ePrints2008Knight, JNing, CThis paper examines the estimation of the Stochastic Conditional Duration model by the empirical characteristic function and the generalized method of moments when maximum likelihood is unavailable. The joint characteristic function for the durations along with general expressions for the moments are derived, leading naturally to estimation via the empirical characteristic function and generalized method of moments. In a Monte Carlo study as well as an empirical application, these alternative methods are compared with quasi maximum likelihood. These experiments reveal that the empirical characteristic function approach outperforms the quasi maximum likelihood and generalized method of moments in terms of both bias and root mean square error. |
| spellingShingle | Knight, J Ning, C Estimation of the Stochastic Conditional Duration Model via Alternative Methods. |
| title | Estimation of the Stochastic Conditional Duration Model via Alternative Methods. |
| title_full | Estimation of the Stochastic Conditional Duration Model via Alternative Methods. |
| title_fullStr | Estimation of the Stochastic Conditional Duration Model via Alternative Methods. |
| title_full_unstemmed | Estimation of the Stochastic Conditional Duration Model via Alternative Methods. |
| title_short | Estimation of the Stochastic Conditional Duration Model via Alternative Methods. |
| title_sort | estimation of the stochastic conditional duration model via alternative methods |
| work_keys_str_mv | AT knightj estimationofthestochasticconditionaldurationmodelviaalternativemethods AT ningc estimationofthestochasticconditionaldurationmodelviaalternativemethods |