TY - JOUR
T1 - High-dimensional conditionally Gaussian state space models with missing data
AU - Chan, Joshua C.C.
AU - Poon, Aubrey
AU - Zhu, Dan
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/9
Y1 - 2023/9
N2 - 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.
AB - 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.
KW - Dynamic factor model
KW - Mixed-frequency
KW - Stochastic volatility
KW - Unbalanced panel
KW - Vector autoregression
UR - http://www.scopus.com/inward/record.url?scp=85161974487&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2023.05.005
DO - 10.1016/j.jeconom.2023.05.005
M3 - Article
AN - SCOPUS:85161974487
SN - 0304-4076
VL - 236
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 1
M1 - 105468
ER -