EconPapers    
Economics at your fingertips  
 

Markov Chain Monte Carlo Simulation Methods in Econometrics

Siddhartha Chib and Edward Greenberg

Econometric Theory, 1996, vol. 12, issue 03, 409-431

Abstract: We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.

Date: 1996
References: Add references at CitEc
Citations View citations in EconPapers (176) Track citations by RSS feed

Downloads: (external link)
http://journals.cambridge.org/abstract_S0266466600006794 link to article abstract page (text/html)

Related works:
Working Paper: Markov Chain Monte Carlo Simulation Methods in Econometrics (1995) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:12:y:1996:i:03:p:409-431_00

Access Statistics for this article

More articles in Econometric Theory from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Series data maintained by Keith Waters ().

 
Page updated 2017-10-01
Handle: RePEc:cup:etheor:v:12:y:1996:i:03:p:409-431_00