Florian Huber, Michael Pfarrhofer
arXiv 14 May 2020 · Econometrics · publishedJournal of Applied Econometrics (2020)
arXiv:2005.06851 · PDF · DOI · OpenAlex · Extracted main text
Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques that allow for time-variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters slightly improves forecast performance for the United States (US), the United Kingdom (UK) and the Euro Area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
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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 | Chan JCC (2017) The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling | 1.000 | 13 | 5 | 100% |
| 2 | Kowal DR, Matteson DS, and Ruppert D (2019) Dynamic shrinkage processes | 0.811 | 4 | 2 | 100% |
| 3 | Frühwirth-Schnatter S, and Wagner H (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models | 0.644 | 2 | 2 | 100% |
| 4 | Makalic E, and Schmidt DF (2015) A simple sampler for the horseshoe estimator | 0.511 | 2 | 1 | 100% |
| 5 | Kastner G, and Frühwirth-Schnatter S (2014) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models | 0.405 | 1 | 1 | 100% |
| 6 | Carvalho CM, Polson NG, and Scott JG (2010) The horseshoe estimator for sparse signals | 0.405 | 1 | 1 | 100% |
| 7 | Carter CK, and Kohn R (1994) On Gibbs sampling for state space models | 0.405 | 1 | 1 | 100% |
| 8 | Frühwirth-Schnatter S (1994) Data augmentation and dynamic linear models | 0.405 | 1 | 1 | 100% |
| 9 | Geweke J, and Amisano G (2010) Comparing and evaluating Bayesian predictive distributions of asset returns | 0.405 | 1 | 1 | 100% |
| 10 | Hou C (2020) Time-Varying Relationship between Inflation and Inflation Uncertainty | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 14 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 Forecasting in Economics and Finance: A Modern Review | 0.405 | 1 | 1 |
| 2 | The Dynamic Triple Gamma as a Shrinkage Process for Time-Varying Parameter Models | 0.405 | 1 | 1 |