Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna, Sylvia Frühwirth-Schnatter
arXiv 16 Jul 2019 · Econometrics · publishedJournal of Statistical Software (2021) · 24 citations (OpenAlex)
arXiv:1907.07065 · PDF · DOI · OpenAlex · Extracted main text
Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TVP models is well known. This issue can be dealt with using appropriate global-local shrinkage priors, which pull time-varying parameters towards static ones. In this paper, we introduce the R package shrinkTVP (Knaus, Bitto-Nemling, Cadonna, and Fr\"uhwirth-Schnatter 2019), which provides a fully Bayesian implementation of shrinkage priors for TVP models, taking advantage of recent developments in the literature, in particular that of Bitto and Fr\"uhwirth-Schnatter (2019). The package shrinkTVP allows for posterior simulation of the parameters through an efficient Markov Chain Monte Carlo (MCMC) scheme. Moreover, summary and visualization methods, as well as the possibility of assessing predictive performance through log predictive density scores (LPDSs), are provided. The computationally intensive tasks have been implemented in C++ and interfaced with R. The paper includes a brief overview of the models and shrinkage priors implemented in the package. Furthermore, core functionalities are illustrated, both with simulated and real data.
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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 | Bitto A, Frühwirth-Schnatter S (2019) Achieving Shrinkage in a Time-Varying Parameter Model Framework | 1.000 | 10 | 4 | 100% |
| 2 | Cadonna A, Frühwirth-Schnatter S, Knaus P (2020) Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models | 0.874 | 9 | 2 | 100% |
| 3 | R Core Team (2017) R: A Language and Environment for Statistical Computing | 0.843 | 3 | 3 | 100% |
| 4 | Primiceri G (2005) Time Varying Structural Vector Autoregressions and Monetary Policy | 0.811 | 4 | 2 | 100% |
| 5 | McCausland WJ, Miller S, Pelletier D (2011) Simulation Smoothing for State Space Models: A Computational Efficiency Analysis | 0.737 | 3 | 3 | 67% |
| 6 | Knaus P, Bitto-Nemling A, Cadonna A, Frühwirth-Schnatter S (2020) shrinkTVP: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage | 0.644 | 2 | 2 | 100% |
| 7 | Krueger F (2015) bvarsv: Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters | 0.644 | 2 | 2 | 100% |
| 8 | Kastner G, Frühwirth-Schnatter S (2014) Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models | 0.585 | 3 | 1 | 100% |
| 9 | Kastner G (2016) Dealing with Stochastic Volatility in Time Series Using the R Package stochvol | 0.585 | 3 | 1 | 100% |
| 10 | Microsoft, Weston S (2018) doParallel: Foreach Parallel Adaptor for the parallel Package | 0.511 | 2 | 2 | 50% |
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 | 0.54cm Time-Varying Parameters as Ridge Regressions | 0.405 | 1 | 1 |
| 2 | The Dynamic Triple Gamma as a Shrinkage Process for Time-Varying Parameter Models | 0.405 | 1 | 1 |