Niko Hauzenberger, Florian Huber, Gary Koop, Luca Onorante
arXiv 23 Oct 2019 · Econometrics · publishedJournal of Business and Economic Statistics (2021) · 31 citations (OpenAlex)
arXiv:1910.10779 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which groups the TVPs into several regimes. These flexible mixtures allow for TVPs that feature a small, moderate or large number of structural breaks. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the KT regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our models to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
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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 | Malsiner-Walli G, Frühwirth-Schnatter S, and Grün B (2016) Model-based clustering based on sparse finite Gaussian mixtures | 0.950 | 7 | 4 | 86% |
| 2 | Griffin J, and Brown P (2010) Inference with normal-gamma prior distributions in regression problems | 0.843 | 4 | 4 | 75% |
| 3 | Kastner G, and Frühwirth-Schnatter S (2014) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models | 0.794 | 6 | 3 | 50% |
| 4 | Trippe B, Huggins J, Agrawal R, and Broderick T (2019) LR-GLM: High-dimensional Bayesian inference using low-rank data approximations, in | 0.644 | 4 | 1 | 100% |
| 5 | Frühwirth-Schnatter S (2001) Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models | 0.644 | 2 | 2 | 100% |
| 6 | Bitto A, and Frühwirth-Schnatter S (2019) Achieving shrinkage in a time-varying parameter model framework | 0.644 | 2 | 2 | 100% |
| 7 | Doan T, Litterman R, and Sims C (1984) Forecasting and conditional projection using realistic prior distributions | 0.644 | 2 | 2 | 100% |
| 8 | Griffin JE, and Brown PJ (2013) Some priors for sparse regression modelling | 0.644 | 2 | 2 | 100% |
| 9 | Huber F, Koop G, and Onorante L (2021) Inducing sparsity and shrinkage in time-varying parameter models | 0.644 | 2 | 2 | 100% |
| 10 | Koop G, Korobilis D, and Pettenuzzo D (2019) Bayesian compressed Vector Autoregressions | 0.644 | 2 | 2 | 100% |
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