High-dimensional conditionally Gaussian state space models with missing data

Joshua Chan, Aubrey Poon and Dan Zhu

Journal of Econometrics, 2023, vol. 236, issue 1

Abstract: 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.

Keywords: Mixed-frequency; Unbalanced panel; Vector autoregression; Dynamic factor model; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C32 C55 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:236:y:2023:i:1:s0304407623001628

DOI: 10.1016/j.jeconom.2023.05.005

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