Open AccessSimulation‐based Likelihood Inference for Limited Dependent Processes
Author(s) -
Manrique Aurora,
Shephard Neil
Publication year - 1998
Publication title -
the econometrics journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.861
H-Index - 36
eISSN - 1368-423X
pISSN - 1368-4221
DOI - 10.1111/1368-423x.11010
Subject(s) - inference , computer science , series (stratigraphy) , dimension (graph theory) , bayesian inference , machine learning , algorithm , artificial intelligence , bayesian probability , data mining , mathematics , paleontology , pure mathematics , biology
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.