High-dimensional conditionally Gaussian state space models with missing data

Joshua C.C. Chan, Aubrey Poon, Dan Zhu

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets and large Bayesian VARs with variables in multiple frequencies. A key observation underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian. Furthermore, the inverse covariance or precision matrix of this conditional distribution is sparse, and this special structure can be exploited to substantially speed up computations. We illustrate the methodology using two empirical applications. The first application combines quarterly, monthly and weekly data using a large Bayesian VAR to produce weekly GDP estimates. In the second application, we extract latent factors from unbalanced datasets involving over a hundred monthly variables via a dynamic factor model with stochastic volatility.

Original languageEnglish
Article number105468
Number of pages21
JournalJournal of Econometrics
Issue number1
Publication statusPublished - Sept 2023


  • Dynamic factor model
  • Mixed-frequency
  • Stochastic volatility
  • Unbalanced panel
  • Vector autoregression

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