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Simulation-based likelihood inference for limited dependent processes

Abstract:
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 which 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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/1368-423X.11010

Authors


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Institution:
University of Salamanca, Spain
Role:
Author
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Institution:
University of Oxford
Division:
SSD
Department:
Economics
Research group:
Econometrics
Oxford college:
Nuffield College
Role:
Author

Contributors

Economic and Social Research Council More from this funder
European Union More from this funder
Publisher:
Blackwell Publishing Publisher's website
Journal:
Econometrics journal Journal website
Volume:
1
Issue:
1
Pages:
174-202
Publication date:
1998-06-01
DOI:
EISSN:
1368-423X
ISSN:
1368-4221
Language:
English
Keywords:
Subjects:
UUID:
uuid:abbbddb6-d93a-4914-b15c-1adc2a0939bf
Local pid:
ora:2262
Deposit date:
2008-08-12

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