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- Title
Adaptive Markov chain Monte Carlo sampling and estimation in Mata.
- Authors
Baker, Matthew J.
- Abstract
I describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) methods; I introduce a Mata function for performing adaptive MCMC, amcmc(); and I present a suite of functions, amcmc *(), that allows an alternative implementation of adaptive MCMC. amcmc() and amcmc *() can be used with models set up to work with Mata's moptimize( ) (see [M-5] moptimize( )) or optimize( ) (see [M-5] optimize( )) or with standalone functions. To show how the routines can be used in estimation problems, I give two examples of what Chernozhukov and Hog (2003, Journal of Econometrics 115: 293-346) refer to as quasi-Bayesian or Laplace-type estimators-simulationbased estimators using MCMC sampling. In the first example, I illustrate basic ideas and show how a simple linear model can be fit by simulation. In the next example, I describe simulation-based estimation of a censored quantile regression model following Powell (1986, Journal of Econometrics 32: 143-155); the discussion describes the workings of the command mcmccqreg. I also present an example of how the routines can be used to draw from distributions without a normalizing constant and used in Bayesian estimation of a mixed logit model. This discussion introduces the command bayesmixedlogit.
- Subjects
MARKOV chain Monte Carlo; ESTIMATION theory; BAYES' estimation; ALGORITHMS; BAYESIAN analysis
- Publication
Stata Journal, 2014, Vol 14, Issue 3, p623
- ISSN
1536-867X
- Publication type
Article
- DOI
10.1177/1536867x1401400309