Simulation‐Based Likelihood Inference for Limited Dependent Processes

29 Pages Posted: 24 Sep 2014

See all articles by Aurora Manrique

Aurora Manrique

University of Salamanca

Neil Shephard

Harvard University

Multiple version iconThere are 2 versions of this paper

Date Written: June 1998

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.

Keywords: Disequilibrium models, Markov chain Monte Carlo, Scan sampler, Tobit model

Suggested Citation

Manrique García, Maria Aurora and Shephard, Neil, Simulation‐Based Likelihood Inference for Limited Dependent Processes (June 1998). The Econometrics Journal, Vol. 1, Issue 1, pp. 174-202, 1998. Available at SSRN: https://ssrn.com/abstract=2500557 or http://dx.doi.org/10.1111/1368-423X.11010

Maria Aurora Manrique García (Contact Author)

University of Salamanca ( email )

Campus Miguel de Unamuno
Dept. of Economics
37008 Salamanca
Spain

Neil Shephard

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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