Joshua C. C. Chan, Aubrey Poon, Dan Zhu
arXiv 21 Dec 2021 · Econometrics · 4 citations (OpenAlex)
arXiv:2112.11315 · PDF · DOI · OpenAlex · Extracted main text
State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computational challenges, we propose two novel precision-based samplers to draw the missing observations of the low-frequency variables in these models, building on recent advances in the band and sparse matrix algorithms for state-space models. We show via a simulation study that the proposed methods are more numerically accurate and computationally efficient compared to standard Kalman-filter based methods. We demonstrate how the proposed method can be applied in two empirical macroeconomic applications: estimating the monthly output gap and studying the response of GDP to a monetary policy shock at the monthly frequency. Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.
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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 and Song (2015) Real-time forecasting with a mixed-frequency VAR | 1.000 | 8 | 4 | 100% |
| 2 | Caldara and Herbst (2019) Monetary policy, real activity, and credit spreads: Evidence from Bayesian proxy SVARs | 0.874 | 9 | 2 | 100% |
| 3 | Morley and Wong (2020) Estimating and accounting for the output gap with large Bayesian vector autoregressions | 0.874 | 5 | 2 | 100% |
| 4 | Chan and Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models self | 0.811 | 4 | 2 | 100% |
| 5 | Mariano and Murasawa (2003) A new coincident index of business cycles based on monthly and quarterly series | 0.737 | 3 | 2 | 100% |
| 6 | Mariano and Murasawa (2010) A coincident index, common factors, and monthly real GDP | 0.737 | 3 | 2 | 100% |
| 7 | Carter and Kohn (1994) On Gibbs Sampling for State Space Models | 0.644 | 4 | 1 | 100% |
| 8 | Bańbura, Giannone, and Reichlin (2010) Large Bayesian vector auto regressions | 0.644 | 2 | 2 | 100% |
| 9 | Cross, Hou, and Poon (2019) Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity self | 0.644 | 2 | 2 | 100% |
| 10 | Koop (2013) Forecasting with medium and large Bayesian VARs | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP | 1.000 | 5 | 3 |