arXiv 18 Jan 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2022) · 26 citations (OpenAlex)
arXiv:2201.07303 · PDF · DOI · OpenAlex · Extracted main text
Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification method for a class of models we call hybrid TVP-VARs--VARs with time-varying parameters in some equations but constant coefficients in others. Specifically, for each equation, the new method automatically decides whether the VAR coefficients and contemporaneous relations among variables are constant or time-varying. Using US datasets of various dimensions, we find evidence that the parameters in some, but not all, equations are time varying. The large hybrid TVP-VAR also forecasts better than many standard benchmarks.
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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, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 1.000 | 9 | 3 | 100% |
| 2 | Cogley and Sargent (2005) Drifts and volatilities: Monetary policies and outcomes in the post WWII US | 1.000 | 8 | 4 | 100% |
| 3 | Carriero, Clark, and Marcellino (2015) Bayesian VARs: Specification choices and forecast accuracy | 1.000 | 5 | 3 | 100% |
| 4 | Primiceri (2005) Time varying structural vector autoregressions and monetary policy | 0.987 | 27 | 5 | 96% |
| 5 | Chan (2021) Minnesota-type adaptive hierarchical priors for large Bayesian VARs self | 0.843 | 4 | 3 | 75% |
| 6 | Frühwirth-Schnatter and Wagner (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models | 0.843 | 3 | 3 | 100% |
| 7 | Diebold and Mariano (1995) Comparing predictive accuracy | 0.781 | 7 | 2 | 71% |
| 8 | Arias, Rubio-Ramirez, and Shin (2021) Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models | 0.737 | 4 | 2 | 75% |
| 9 | Chan and Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models self | 0.737 | 3 | 3 | 67% |
| 10 | D'Agostino, Gambetti, and Giannone (2013) Macroeconomic forecasting and structural change | 0.737 | 3 | 2 | 100% |
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