Dimitris Korobilis, Maximilian Schröder
arXiv 16 May 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2305.09563 · PDF · DOI · OpenAlex · Extracted main text
We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis.
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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 | Chen, L., Dolado, J. J., and Gonzalo, J (2021) Quantile factor models | 1.000 | 6 | 3 | 100% |
| 2 | Bernanke, B. S., Boivin, J., and Eliasz, P (2005) Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach* | 0.965 | 10 | 4 | 90% |
| 3 | Korobilis, D. and Schröder, M (2022) Probabilistic quantile factor analysis self | 0.909 | 12 | 4 | 75% |
| 4 | Adrian, T., Boyarchenko, N., and Giannone, D (2019) Vulnerable growth | 0.874 | 5 | 2 | 100% |
| 5 | Stock, J. H. and Watson, M. W (2005) Implications of dynamic factor models for var analysis | 0.874 | 5 | 2 | 100% |
| 6 | Plagborg-Mller, M., Reichlin, L., Ricco, G., and Hasenzagl, T (2020) When is Growth at Risk? | 0.811 | 4 | 2 | 100% |
| 7 | Carter, C. K. and Kohn, R (1994) On gibbs sampling for state space models | 0.585 | 3 | 3 | 33% |
| 8 | Diebold, F. X. and Ylmaz, K (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms | 0.585 | 3 | 1 | 100% |
| 9 | Kose, M. A., Otrok, C., and Whiteman, C. H (2003) International business cycles: World, region, and country-specific factors | 0.585 | 3 | 1 | 100% |
| 10 | Lütkepohl, H (2005) New Introduction to Multiple Time Series Analysis | 0.511 | 2 | 2 | 50% |
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