Adaptive Markov chain Monte Carlo sampling and estimation in Mata
AbstractI describe algorithms for drawing from distributions using adaptive Markov chain Monte Carlo (MCMC) methods, introduce a Mata function for per- forming adaptive MCMC, amcmc(), and a suite of functions amcmc_*() allowing an implementation of adaptive MCMC using a structure. To ease use in application to estimation problems, amcmc() and amcmc_*() can be used in conjunction with models set up to work with Mata’s moptimize( ) or optimize( ), or with stand-alone functions. I apply the routines in a simple estimation problem, in drawing from a distributions without a normalizing constant, and in Bayesian estimation of a mixed logit model.
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Bibliographic InfoPaper provided by Hunter College: Department of Economics in its series Hunter College Department of Economics Working Papers with number 440.
Date of creation: 2013
Date of revision:
Stata; Mata; Markov chain Monte Carlo; drawing from distributions; Bayesian estimation; mixed logit;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
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- Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
- Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
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