Variance–covariance from a metropolis chain on a curved, singular manifold

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We consider estimation of variance and covariance from a point cloud that are draws from a posterior distribution that lie on a curved, singular manifold. The motivating application is Bayesian inference regarding a likelihood subject to overidentified moment equations using MCMC (Markov Chain Monte Carlo). The MCMC draws lie on a singular manifold that typically is curved. Variance and covariance are Euclidean concepts. A curved, singular manifold is not typically a Euclidean space. We explore some suggestions on how to adapt a Euclidean concept to a non-Euclidean space then build on them to propose and illustrate appropriate methods.

Original languageEnglish (US)
Pages (from-to)843-861
Number of pages19
JournalJournal of Econometrics
Issue number2
StatePublished - Aug 2023

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Economics and Econometrics


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