Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/94343 
Year of Publication: 
1998
Series/Report no.: 
Working Paper No. 1998-17
Publisher: 
Rutgers University, Department of Economics, New Brunswick, NJ
Abstract: 
This paper examines maximum likelihood estimation via hill climbing and the expectations maximization (EM) algorithm in the context of Hamilton's Markov switching framework. The techniques are explained in detail and are followed by a discussion of both analytic and computational issues. Both algorithms tend to be computer intensive, but an approximation technique is shown to significantly reduce the computational demands of the EM algorithm relative to a Davidon Fletcher Powell hill-climbing routine.
Subjects: 
EM algorithm
Markov switching
JEL: 
C22
Document Type: 
Working Paper

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