Adaptive Markov chain Monte Carlo sampling and estimation in Mata
I 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.
|Date of creation:||2013|
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- Train,Kenneth E., 2009.
"Discrete Choice Methods with Simulation,"
Cambridge University Press, number 9780521766555, January.
- Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, June.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
- Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
- Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August. Full references (including those not matched with items on IDEAS)
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