arXiv 17 Jun 2020 · Econometrics · publishedEconometrics and Statistics (2021) · 2 citations (OpenAlex)
arXiv:2006.10088 · PDF · DOI · OpenAlex · Extracted main text
Time-varying parameter (TVP) models often assume that the TVPs evolve according to a random walk. This assumption, however, might be questionable since it implies that coefficients change smoothly and in an unbounded manner. In this paper, we relax this assumption by proposing a flexible law of motion for the TVPs in large-scale vector autoregressions (VARs). Instead of imposing a restrictive random walk evolution of the latent states, we carefully design hierarchical mixture priors on the coefficients in the state equation. These priors effectively allow for discriminating between periods where coefficients evolve according to a random walk and times where the TVPs are better characterized by a stationary stochastic process. Moreover, this approach is capable of introducing dynamic sparsity by pushing small parameter changes towards zero if necessary. The merits of the model are illustrated by means of two applications. Using synthetic data we show that our approach yields precise parameter estimates. When applied to US data, the model reveals interesting patterns of low-frequency dynamics in coefficients and forecasts well relative to a wide range of competing models.
appendix boundary found by appendix_command · 83% of the source is main text. Read the extracted text to check this.
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 | Hauzenberger N, Huber F, and Koop G (2020) Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods | 1.000 | 7 | 3 | 100% |
| 2 | Huber F, Kastner G, and Feldkircher M (2019) Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models | 1.000 | 6 | 3 | 100% |
| 3 | Hauzenberger N, Huber F, Koop G, and Onorante L (2019) Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models | 0.977 | 15 | 6 | 93% |
| 4 | Malsiner-Walli G, Frühwirth-Schnatter S, and Grün B (2016) Model-based clustering based on sparse finite Gaussian mixtures | 0.894 | 7 | 3 | 71% |
| 5 | Huber F, Koop G, and Pfarrhofer M (2020) b), Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations | 0.874 | 5 | 2 | 100% |
| 6 | Chan JC, and Jeliazkov I (2009) Efficient simulation and integrated likelihood estimation in state space models | 0.843 | 3 | 3 | 100% |
| 7 | Huber F, Koop G, and Onorante L (2020) a), Inducing sparsity and shrinkage in time-varying parameter models | 0.843 | 3 | 3 | 100% |
| 8 | Griffin J, and Brown P (2010) Inference with normal-gamma prior distributions in regression problems | 0.794 | 6 | 4 | 50% |
| 9 | Bhattacharya A, Chakraborty A, and Mallick BK (2016) Fast sampling with Gaussian scale mixture priors in high-dimensional regression | 0.737 | 3 | 2 | 100% |
| 10 | Koop G, Leon-Gonzalez R, and Strachan RW (2009) On the evolution of the monetary policy transmission mechanism | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 78 scored citations.