Sebastian Ankargren, Paulina Jonéus
arXiv 1 Jul 2019 · Econometrics · publishedEconometrics and Statistics (2020) · 4 citations (OpenAlex)
arXiv:1907.01075 · PDF · DOI · OpenAlex · Extracted main text
There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macroeconomic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.
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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 | Carriero, A., Clark, T. E., and Marcellino, M (2019) Large Vector Autoregressions with Stochastic Volatility and Non-Conjugate Priors | 1.000 | 5 | 3 | 100% |
| 2 | Götz, T. B. and Hauzenberger, K (2018) Large Mixed-Frequency VARs with a Parsimonious Time-Varying Parameter Structure | 1.000 | 5 | 3 | 100% |
| 3 | Schorfheide, F. and Song, D (2015) Real-Time Forecasting with a Mixed-Frequency VAR | 0.988 | 29 | 6 | 97% |
| 4 | Bańbura, M., Giannone, D., and Reichlin, L (2010) Large Bayesian Vector Auto Regressions | 0.928 | 4 | 3 | 100% |
| 5 | Ankargren, S., Unosson, M., and Yang, Y (2018) A Mixed-Frequency Bayesian Vector Autoregression with a Steady-State Prior self | 0.644 | 2 | 2 | 100% |
| 6 | Carter, C. K. and Kohn, R (1994) On Gibbs Sampling for State Space Models | 0.644 | 2 | 2 | 100% |
| 7 | Cimadomo, J. and D'Agostino, A (2016) Combining Time Variation and Mixed Frequencies: An Analysis of Government Spending Multipliers in Italy | 0.644 | 2 | 2 | 100% |
| 8 | McCracken, M. W. and Ng, S (2016) FRED-MD: A Monthly Database for Macroeconomic Research | 0.644 | 2 | 2 | 100% |
| 9 | Qian, H (2016) A computationally efficient method for vector autoregression with mixed frequency data | 0.644 | 2 | 2 | 100% |
| 10 | Eraker, B., Chiu, C. W., Foerster, A. T., Kim, T. B., and Seoane, H. D (2015) Bayesian Mixed Frequency VARs | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 31 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nowcasting using regression on signatures | 0.405 | 1 | 1 |
| 2 | Monthly GDP Growth Estimates for the U.S. States | 0.000 | 1 | 1 |