arXiv 12 Sep 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2409.08354 · PDF · DOI · OpenAlex · Extracted main text
We introduce a class of Bayesian matrix dynamic factor models that accommodates time-varying volatility, outliers, and cross-sectional correlation in the idiosyncratic components. For model comparison, we employ an importance-sampling estimator of the marginal likelihood based on the cross-entropy method to determine: (1) the optimal dimension of the factor matrix; (2) whether a vector- or matrix-valued structure is more suitable; and (3) whether an approximate or exact factor model is favored by the data. Through a series of Monte Carlo experiments, we demonstrate the accuracy of the factor estimates and the effectiveness of the marginal likelihood estimator in correctly identifying the true model. Applications to macroeconomic and financial datasets illustrate the model's ability to capture key features in matrix-valued time series.
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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 | Wang, D., X. Liu, and R. Chen (2019) Factor models for matrix-valued high-dimensional time series | 1.000 | 5 | 3 | 100% |
| 2 | Chan, J. C. and E. Eisenstat (2015) Marginal likelihood estimation with the cross-entropy method | 0.811 | 4 | 2 | 100% |
| 3 | Chamberlain, G. and M. Rothschild (1983) Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets | 0.737 | 3 | 2 | 100% |
| 4 | Chan, J. C (2023) Comparing stochastic volatility specifications for large Bayesian VARs | 0.737 | 3 | 2 | 100% |
| 5 | He, Y., X. Kong, L. Yu, X. Zhang, and C. Zhao (2024) Matrix factor analysis: From least squares to iterative projection | 0.737 | 3 | 2 | 100% |
| 6 | Nobile, A (2000) Comment: Bayesian multinomial probit models with a normalization constraint | 0.644 | 4 | 2 | 50% |
| 7 | Cong, Y., B. Chen, and M. Zhou (2004) Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions | 0.644 | 3 | 2 | 67% |
| 8 | Stock, J. H. and M. W. Watson (2016) Core inflation and trend inflation | 0.644 | 3 | 2 | 67% |
| 9 | Chan, J. C. and Y. Qi (2024) Large Bayesian Matrix Autoregressions | 0.511 | 2 | 2 | 50% |
| 10 | Yu, L., Y. He, X. Kong, and X. Zhang (2022) Projected estimation for large-dimensional matrix factor models | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Large Bayesian Tensor Autoregressions | 0.405 | 1 | 1 |