Matteo Iacopini, Aubrey Poon, Luca Rossini, Dan Zhu
arXiv 5 Sep 2022 · Econometrics · publishedJournal of Economic Dynamics and Control (2023) · 12 citations (OpenAlex)
arXiv:2209.01910 · PDF · DOI · OpenAlex · Extracted main text
Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low-frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation. The proposed methods allow us to nowcast conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk and the Expected Shortfall.
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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 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 1.000 | 8 | 3 | 100% |
| 2 | Chan, J. C., A. Poon, and D. Zhu (2021) Efficient estimation of state-space mixed-frequency VARs: A precision-based approach | 1.000 | 5 | 3 | 100% |
| 3 | Petrella, L. and V. Raponi (2019) Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress | 0.928 | 5 | 3 | 80% |
| 4 | Schorfheide, F. and D. Song (2015) Real-time forecasting with a mixed-frequency VAR | 0.811 | 4 | 2 | 100% |
| 5 | Chan, J. C. and I. Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models | 0.644 | 2 | 2 | 100% |
| 6 | Cong, Y., B. Chen, and M. Zhou (2017) Fast simulation of hyperplane-truncated multivariate normal distributions | 0.644 | 2 | 2 | 100% |
| 7 | Kotz, S., T. Kozubowski, and K. Podgórski (2001) The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance | 0.511 | 2 | 2 | 50% |
| 8 | Gneiting, T. and R. Ranjan (2011) Comparing density forecasts using threshold- and quantile-weighted scoring rules | 0.405 | 1 | 1 | 100% |
| 9 | Huber, F. and L. Rossini (2022) Inference in bayesian additive vector autoregressive tree models | 0.405 | 1 | 1 | 100% |
| 10 | Chavleishvili, S. and S. Manganelli (2021) Forecasting and Stress Testing with Quantile Vector Autoregression | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 46 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications | 0.405 | 1 | 1 |