Florian Huber, Gary Koop, Luca Onorante
arXiv 26 May 2019 · Econometrics · publishedJournal of Business and Economic Statistics (2020) · 84 citations (OpenAlex)
arXiv:1905.10787 · PDF · DOI · OpenAlex · Extracted main text
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to reduce this uncertainty and improve forecasts. In this paper, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
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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 | Ray P and Bhattacharya A (2018) Signal Adaptive Variable Selector fo… arXiv preprint arXiv:1810.09004 | 1.000 | 12 | 3 | 100% |
| 2 | Woody S, Carvalho C and Murray J (2019) Model interpretation through… arXiv:1905.07103v3 | 1.000 | 7 | 3 | 100% |
| 3 | Hahn PR and Carvalho CM (2015) Decoupling Shrinkage and Selection in… Journal of the American Statistical Association 110(509), 435–448 | 1.000 | 6 | 3 | 100% |
| 4 | Bhattacharya A, Pati D, Pillai NS and Dunson DB (2015) Dirichlet–Lap… Journal of the American Statistical Association 110(512), 1479–1490 | 0.794 | 6 | 4 | 50% |
| 5 | Griffin J and Brown P (2010) Inference with normal-gamma prior distr… Bayesian Analysis 5(1), 171–188 | 0.794 | 6 | 4 | 50% |
| 6 | Ishwaran H and Rao JS (2005) Spike and slab variable selection: freq… The Annals of Statistics 33(2), 730–773 | 0.794 | 6 | 4 | 50% |
| 7 | Carvalho CM, Polson NG and Scott JG (2010) The horseshoe estimator f… Biometrika 97(2), 465–480 | 0.737 | 3 | 3 | 67% |
| 8 | George EI and McCulloch RE (1993) Variable selection via Gibbs sampl… Journal of the American Statistical Association 88(423), 881–889 | 0.737 | 3 | 3 | 67% |
| 9 | Park T and Casella G (2008) The Bayesian Lasso Journal of the American Statistical Association 103(482), 681–686 | 0.737 | 3 | 3 | 67% |
| 10 | Kastner G and Huber F (2017) Sparse Bayesian vector autoregressions… manuscript | 0.737 | 3 | 2 | 100% |
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