Tibor Szendrei, Arnab Bhattacharjee, Mark E. Schaffer
arXiv 21 Jun 2024 · Econometrics
arXiv:2406.15157 · PDF · DOI · OpenAlex · Extracted main text
Mixed frequency data has been shown to improve the performance of growth-at-risk models in the literature. Most of the research has focused on imposing structure on the high-frequency lags when estimating MIDAS-QR models akin to what is done in mean models. However, only imposing structure on the lag-dimension can potentially induce quantile variation that would otherwise not be there. In this paper we extend the framework by introducing structure on both the lag dimension and the quantile dimension. In this way we are able to shrink unnecessary quantile variation in the high-frequency variables. This leads to more gradual lag profiles in both dimensions compared to the MIDAS-QR and UMIDAS-QR. We show that this proposed method leads to further gains in nowcasting and forecasting on a pseudo-out-of-sample exercise on US data.
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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 | Ferrara, L., M. Mogliani, and J.-G. Sahuc (2022) High-frequency monitoring of growth at risk | 1.000 | 19 | 4 | 100% |
| 2 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 1.000 | 16 | 4 | 100% |
| 3 | Kohns, D. and T. Szendrei (2023) Horseshoe prior Bayesian quantile regression | 1.000 | 6 | 3 | 100% |
| 4 | Mitchell, J., A. Poon, and D. Zhu (2022) Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics | 1.000 | 6 | 3 | 100% |
| 5 | Xu, Q., M. Xu, C. Jiang, and W. Fu (2023) Mixed-frequency growth-at-risk with the midas-qr method: Evidence from china | 1.000 | 6 | 3 | 100% |
| 6 | Szendrei, T., A. Bhattacharjee, and M. E. Schaffer (2024) Fused LASSO as non-crossing quantile regression self | 0.874 | 9 | 2 | 100% |
| 7 | Lima, L. R., F. Meng, and L. Godeiro (2020) Quantile forecasting with mixed-frequency data | 0.874 | 6 | 2 | 100% |
| 8 | Diebold, F. X. and R. S. Mariano (1995) Comparing predictive accuracy | 0.843 | 4 | 3 | 75% |
| 9 | Gneiting, T. and R. Ranjan (2011) Comparing density forecasts using threshold-and quantile-weighted scoring rules | 0.811 | 4 | 2 | 100% |
| 10 | Koenker, R. and G. Bassett (1978) Regression quantiles | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 31 scored citations.