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High-dimensional conditionally Gaussian state space models with missing data

Chan, Joshua, Poon, Aubrey, Zhu, Dan (2023) High-dimensional conditionally Gaussian state space models with missing data. Journal of Econometrics, 236 (1). Article Number 105468. ISSN 0304-4076. (doi:10.1016/j.jeconom.2023.05.005) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:103834)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1016/j.jeconom.2023.05.005

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.

Item Type: Article
DOI/Identification number: 10.1016/j.jeconom.2023.05.005
Subjects: H Social Sciences
Divisions: Divisions > Division of Human and Social Sciences > School of Economics
Funders: Örebro University (https://ror.org/05kytsw45)
Depositing User: Aubrey Poon
Date Deposited: 09 Nov 2023 12:04 UTC
Last Modified: 10 Nov 2023 10:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/103834 (The current URI for this page, for reference purposes)

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