help mcmcconverge
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Syntax

        mcmcconverge varlist [if] [in], iter(varname) chain(varname) saving(
            filename) [replace]

    options               Description
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      iter(varname)       variable identifying iterations of each Markov
                            chain
      chain(varname)      variable identifying independent Markov chains
      saving(filename)    location to save file of results
      replace             overwrite existing results file

Description

    mcmcconverge is a command for assessing the convergence of Markov chains
    in Markov Chain Monte Carlo (MCMC) estimation.  It calculates the
    convergence statistics described in section 11.6 of Gelman et al. (2003).

    The command assumes that you begin with a dataset in memory containing
    sequences of draws from two or more Markov chains.  The variable
    specified in the chain() option should identify chains, and the variable
    specified in the iter() option should identify iterations within each
    chain.  Each variable in varlist should contain draws of a different
    scalar estimand.  The data should be arranged as a panel in long form,
    where chain identifies panels and iter identifies observations within
    panels.  This panel is required to be balanced after any restrictions
    specified in the if/in option are applied.

    The command saves results in the file specified by saving(filename).
    Each observation in the results file corresponds to a different scalar
    estimand.  Each variable in the results file contains a different
    convergence statistic.

    The convergence statistics are:

      B                    The between-sequence variance.
      W                    The within-sequence variance.
      varplus              The marginal posterior variance of the estimand.
      Rhat                 The potential scale reduction from further
                            simulations; convergence is achieved when Rhat is
                            near 1.
      neff                 The effective number of independent draws.
      neffmin              min(neffmin,mn), where m is the number of chains
                            and n is the number of iterations per chain.

Author

    Sam Schulhofer-Wohl, Federal Reserve Bank of Minneapolis,
    sschulh1.work@gmail.com.  The views expressed herein are those of the
    author and not necessarily those of the Federal Reserve Bank of
    Minneapolis or the Federal Reserve System.

Reference

    Gelman, Andrew, John B. Carlin, Hal S. Stern and Donald B. Rubin, 2003.
        Bayesian Data Analysis, 2nd ed. New York: Chapman & Hall.