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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.

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File URL: http://econ.hunter.cuny.edu/wp-content/uploads/sites/6/RePEc/papers/HunterEconWP440.pdf
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Paper provided by Hunter College Department of Economics in its series Economics Working Paper Archive at Hunter College with number 440.

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Date of creation: 2013
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Handle: RePEc:htr:hcecon:440
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  1. repec:cup:cbooks:9780521747387 is not listed on IDEAS
  2. repec:cup:cbooks:9780521766555 is not listed on IDEAS
  3. Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
  4. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
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