Sebastian Ankargren, Paulina Jonéus
arXiv 4 Dec 2019 · Econometrics · 2 citations (OpenAlex)
arXiv:1912.02231 · PDF · DOI · OpenAlex · Extracted main text
We discuss the issue of estimating large-scale vector autoregressive (VAR) models with stochastic volatility in real-time situations where data are sampled at different frequencies. In the case of a large VAR with stochastic volatility, the mixed-frequency data warrant an additional step in the already computationally challenging Markov Chain Monte Carlo algorithm used to sample from the posterior distribution of the parameters. We suggest the use of a factor stochastic volatility model to capture a time-varying error covariance structure. Because the factor stochastic volatility model renders the equations of the VAR conditionally independent, settling for this particular stochastic volatility model comes with major computational benefits. First, we are able to improve upon the mixed-frequency simulation smoothing step by leveraging a univariate and adaptive filtering algorithm. Second, the regression parameters can be sampled equation-by-equation in parallel. These computational features of the model alleviate the computational burden and make it possible to move the mixed-frequency VAR to the high-dimensional regime. We illustrate the model by an application to US data using our mixed-frequency VAR with 20, 34 and 119 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 Song, D (2015) Real-Time Forecasting with a Mixed-Frequency VAR | 1.000 | 16 | 4 | 100% |
| 2 | Kastner, G. and Huber, F (2018) Sparse Bayesian Vector Autoregressions in Huge Dimensions | 1.000 | 9 | 5 | 100% |
| 3 | Götz, T. B. and Hauzenberger, K (2018) Large Mixed-Frequency VARs with a Parsimonious Time-Varying Parameter Structure | 1.000 | 8 | 4 | 100% |
| 4 | Carriero, A., Clark, T. E., and Marcellino, M (2019) Large Vector Autoregressions with Stochastic Volatility and Non-Conjugate Priors | 1.000 | 7 | 4 | 100% |
| 5 | Ankargren, S., Unosson, M., and Yang, Y (2019) A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior self | 0.928 | 4 | 3 | 100% |
| 6 | Ankargren, S. and Jonéus, P (2019) Simulation smoothing for large mixed-frequency VARs self | 0.920 | 9 | 4 | 78% |
| 7 | Cogley, T. and Sargent, T. J (2005) Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US | 0.874 | 5 | 2 | 100% |
| 8 | McCracken, M. W. and Ng, S (2016) FRED-MD: A Monthly Database for Macroeconomic Research | 0.843 | 4 | 3 | 75% |
| 9 | Cimadomo, J. and D'Agostino, A (2016) Combining Time Variation and Mixed Frequencies: An Analysis of Government Spending Multipliers in Italy | 0.843 | 3 | 3 | 100% |
| 10 | Koopman, S. J. and Durbin, J (2000) Fast Filtering and Smoothing for Multivariate State Space Models | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 48 scored citations.