Florian Huber, Gregor Kastner, Martin Feldkircher
arXiv 15 Jul 2016 · Statistics — Methodology
arXiv:1607.04532 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a straightforward algorithm to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each coefficient in the system. We significantly decrease the computational burden by approximating the latent indicators that drive the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. The merits of our approach are illustrated with two applications. First, we forecast the US term structure of interest rates and demonstrate forecast gains of the proposed mixture innovation model relative to other benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening.
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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 | Primiceri (2005) Time varying structural vector autoregressions and monetary policy | 0.928 | 5 | 5 | 80% |
| 2 | Gerlach, Carter, and Kohn (2000) Efficient Bayesian inference for dynamic mixture models | 0.928 | 4 | 3 | 100% |
| 3 | Koop, Leon-Gonzalez, and Strachan (2009) On the evolution of the monetary policy transmission mechanism | 0.928 | 4 | 3 | 100% |
| 4 | Nakajima and West (2013) Bayesian analysis of latent threshold dynamic models | 0.928 | 4 | 3 | 100% |
| 5 | Diebold and Li (2006) Forecasting the term structure of government bond yields | 0.874 | 5 | 2 | 100% |
| 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 | McCulloch and Tsay (1993) Bayesian inference and prediction for mean and variance shifts in autoregressive time series | 0.811 | 4 | 2 | 100% |
| 8 | Cogley and Sargent (2005) Drifts and volatilities: monetary policies and outcomes in the post WWII US | 0.644 | 2 | 2 | 100% |
| 9 | Giordani and Kohn (2012) Efficient Bayesian inference for multiple change-point and mixture innovation models | 0.644 | 2 | 2 | 100% |
| 10 | Griffin and Brown (2010) Inference with normal-gamma prior distributions in regression problems | 0.644 | 2 | 2 | 100% |
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