Simulation-Based Likelihood Inference for Limited Dependent Processes

Posted: 22 Jul 1999

See all articles by Aurora Manrique

Aurora Manrique

University of Salamanca

Neil Shephard

Harvard University

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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.

JEL Classification: C10, C15

Suggested Citation

Manrique García, Maria Aurora and Shephard, Neil, Simulation-Based Likelihood Inference for Limited Dependent Processes. The Econometrics Journal, Vol. 1, 1998. Available at SSRN: https://ssrn.com/abstract=156715

Maria Aurora Manrique García

University of Salamanca ( email )

Campus Miguel de Unamuno
Dept. of Economics
37008 Salamanca
Spain

Neil Shephard (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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