Joshua C. C. Chan, Aubrey Poon, Dan Zhu
arXiv 7 Feb 2023 · Econometrics · publishedJournal of Econometrics (2023) · 20 citations (OpenAlex)
arXiv:2302.03172 · PDF · DOI · OpenAlex · Extracted main text
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 insight underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian. Moreover, 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Schorfheide and Song (2015) Real-time forecasting with a mixed-frequency VAR | 1.000 | 10 | 3 | 100% |
| 2 | McCracken and Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.874 | 5 | 2 | 100% |
| 3 | Carriero, Clark, Marcellino, and Mertens (2022) Addressing COVID-19 outliers in BVARs with stochastic volatility | 0.843 | 3 | 3 | 100% |
| 4 | Chan (2020) Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure self | 0.843 | 3 | 3 | 100% |
| 5 | Lewis, Mertens, Stock, and Trivedi (2022) Modeling macroeconomic variations after COVID-19 | 0.737 | 4 | 2 | 75% |
| 6 | Antolin-Diaz, Drechsel, and Petrella (2021) Advances in nowcasting economic activity: Secular trends, large shocks and new data | 0.737 | 3 | 2 | 100% |
| 7 | Aruoba, Diebold, and Scotti (2009) Real-time measurement of business conditions | 0.737 | 3 | 2 | 100% |
| 8 | Carriero, Clark, and Marcellino (2016) Common drifting volatility in large Bayesian VARs | 0.737 | 3 | 2 | 100% |
| 9 | Chan and Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models self | 0.737 | 3 | 2 | 100% |
| 10 | Mariano and Murasawa (2003) A new coincident index of business cycles based on monthly and quarterly series | 0.737 | 3 | 2 | 100% |
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