Data Augmentation in Limited-Dependent Variable Models

Roberto Leon-Gonzalez

Discussion Papers from Department of Economics, University of York

Abstract: This paper proposes a scheme that speeds up the convergence of Markov Chain Monte Carlo (MCMC) algorithms in the context of limited-dependent variable models. The algorithm reduces autocorrelations more than the recently proposed Parameter Expansion Data Augumentation (PX-DA) algorithm. In addition, the paper provides an algorithm to sample a variance-covariance matrix with restrictions directly from the conditional posterior distribution. Finally, it is shown that the PX-DA algorithm, as applied to the multivariate probit model, can be seen as sampling from a different parameterization of the model. However, in some cases the PX-DA algorithm is not invariant to reparameterizations, and a slightly different algorithm is proposed.

Keywords: data augmentation; parameter-expansion-data-augmentation; inverted wishart; multivariate probit; reparameterization. (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:yorken:02/09

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