Sebastian Ankargren, Måns Unosson, Yukai Yang
arXiv 20 Nov 2019 · Econometrics · publishedJournal of Time Series Econometrics (2020) · 13 citations (OpenAlex)
arXiv:1911.09151 · PDF · DOI · OpenAlex · Extracted main text
We propose a Bayesian vector autoregressive (VAR) model for mixed-frequency data. Our model is based on the mean-adjusted parametrization of the VAR and allows for an explicit prior on the 'steady states' (unconditional means) of the included variables. Based on recent developments in the literature, we discuss extensions of the model that improve the flexibility of the modeling approach. These extensions include a hierarchical shrinkage prior for the steady-state parameters, and the use of stochastic volatility to model heteroskedasticity. We put the proposed model to use in a forecast evaluation using US data consisting of 10 monthly and 3 quarterly variables. The results show that the predictive ability typically benefits from using mixed-frequency data, and that improvements can be obtained for both monthly and quarterly variables. We also find that the steady-state prior generally enhances the accuracy of the forecasts, and that accounting for heteroskedasticity by means of stochastic volatility usually provides additional improvements, although not for all variables.
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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, F. and D. Song (2015) Real-time forecasting with a mixed-frequency VAR | 1.000 | 13 | 4 | 100% |
| 2 | Louzis, D. P (2019) Steady-state modeling and macroeconomic forecasting quality | 1.000 | 10 | 5 | 100% |
| 3 | Villani, M (2009) Steady state priors for vector autoregressions | 1.000 | 10 | 4 | 100% |
| 4 | Carriero, A., T. E. Clark, and M. Marcellino (2016) Common drifting volatility in large Bayesian VARs | 1.000 | 7 | 5 | 100% |
| 5 | Clark, T. E (2011) Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility | 1.000 | 6 | 4 | 100% |
| 6 | Huber, F. and M. Feldkircher (2019) Adaptive shrinkage in Bayesian vector autoregressive models | 0.811 | 4 | 2 | 100% |
| 7 | Adolfson, M., J. Lindé, and M. Villani (2007) Forecasting performance of an open economy DSGE model | 0.737 | 3 | 2 | 100% |
| 8 | Bańbura, M., D. Giannone, and L. Reichlin (2010) Large Bayesian vector auto regressions | 0.644 | 2 | 2 | 100% |
| 9 | Clark, T. E. and F. Ravazzolo (2015) Macroeconomic forecasting performance under alternative specifications of time-varying volatility | 0.644 | 2 | 2 | 100% |
| 10 | Eraker, B., C. W. Chiu, A. T. Foerster, T. B. Kim, and H. D. Seoane (2015) Bayesian mixed frequency VARs | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 1912.02231 | 0.928 | 4 | 3 |
| 2 | Sparse Bayesian Vector Autoregressions in Huge Dimensions | 0.405 | 1 | 1 |
| 3 | Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model | 0.405 | 1 | 1 |