Simulation-Based Likelihood Inference for Limited Dependent Processes.
This paper looks at the problem of performing likelihood inference for limited dependent processes. Throughout we use simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis. A common theme is to develop computationally robust...
| Main Authors: | , |
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| 格式: | Journal article |
| 語言: | English |
| 出版: |
1998
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| _version_ | 1826274729439264768 |
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| author | Manrique, A Shephard, N |
| author_facet | Manrique, A Shephard, N |
| author_sort | Manrique, A |
| collection | OXFORD |
| description | This paper looks at the problem of performing likelihood inference for limited dependent processes. Throughout we use simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis. A common theme is to develop computationally robust methods that are likely to perform well for any time series problem. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation signal smoother. |
| first_indexed | 2024-03-06T22:47:53Z |
| format | Journal article |
| id | oxford-uuid:5dd04045-0c48-45f1-a18d-88571aadd82b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:47:53Z |
| publishDate | 1998 |
| record_format | dspace |
| spelling | oxford-uuid:5dd04045-0c48-45f1-a18d-88571aadd82b2022-03-26T17:36:34ZSimulation-Based Likelihood Inference for Limited Dependent Processes.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5dd04045-0c48-45f1-a18d-88571aadd82bEnglishDepartment of Economics - ePrints1998Manrique, AShephard, NThis paper looks at the problem of performing likelihood inference for limited dependent processes. Throughout we use simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis. A common theme is to develop computationally robust methods that are likely to perform well for any time series problem. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation signal smoother. |
| spellingShingle | Manrique, A Shephard, N Simulation-Based Likelihood Inference for Limited Dependent Processes. |
| title | Simulation-Based Likelihood Inference for Limited Dependent Processes. |
| title_full | Simulation-Based Likelihood Inference for Limited Dependent Processes. |
| title_fullStr | Simulation-Based Likelihood Inference for Limited Dependent Processes. |
| title_full_unstemmed | Simulation-Based Likelihood Inference for Limited Dependent Processes. |
| title_short | Simulation-Based Likelihood Inference for Limited Dependent Processes. |
| title_sort | simulation based likelihood inference for limited dependent processes |
| work_keys_str_mv | AT manriquea simulationbasedlikelihoodinferenceforlimiteddependentprocesses AT shephardn simulationbasedlikelihoodinferenceforlimiteddependentprocesses |