Florian Huber, Gary Koop, Michael Pfarrhofer
arXiv 24 Feb 2020 · Econometrics · 7 citations (OpenAlex)
arXiv:2002.10274 · PDF · DOI · OpenAlex · Extracted main text
Researchers increasingly wish to estimate time-varying parameter (TVP) regressions which involve a large number of explanatory variables. Including prior information to mitigate over-parameterization concerns has led to many using Bayesian methods. However, Bayesian Markov Chain Monte Carlo (MCMC) methods can be very computationally demanding. In this paper, we develop computationally efficient Bayesian methods for estimating TVP models using an integrated rotated Gaussian approximation (IRGA). This exploits the fact that whereas constant coefficients on regressors are often important, most of the TVPs are often unimportant. Since Gaussian distributions are invariant to rotations we can split the the posterior into two parts: one involving the constant coefficients, the other involving the TVPs. Approximate methods are used on the latter and, conditional on these, the former are estimated with precision using MCMC methods. In empirical exercises involving artificial data and a large macroeconomic data set, we show the accuracy and computational benefits of IRGA methods.
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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 | Huber F, Koop G, and Onorante L (forthcoming), Inducing sparsity and… | 1.000 | 5 | 3 | 100% |
| 2 | Korobilis D (2019) High-dimensional macroeconomic forecasting using message passing algorithms | 0.874 | 7 | 2 | 100% |
| 3 | van den Boom W, Reeves G, and Dunson DB (2019) Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation | 0.874 | 5 | 2 | 100% |
| 4 | Rangan S, Schniter P, and Fletcher AK (2019) Vector approximate message passing | 0.737 | 3 | 2 | 100% |
| 5 | Bitto A, and Frühwirth-Schnatter S (2019) Achieving shrinkage in a time-varying parameter model framework | 0.644 | 2 | 2 | 100% |
| 6 | McCracken MW, and Ng S (2016) FRED-MD: A monthly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 7 | Griffin J, and Brown P (2010) Inference with normal-gamma prior distributions in regression problems | 0.511 | 2 | 1 | 100% |
| 8 | Kim S, Shephard N, and Chib S (1998) Stochastic volatility: likelihood inference and comparison with ARCH models | 0.511 | 2 | 1 | 100% |
| 9 | Zou X, Li F, Fang J, and Li H (2016) Computationally efficient sparse Bayesian learning via generalized approximate message passing, in 2016 IEEE International Confe… | 0.511 | 2 | 1 | 100% |
| 10 | Belmonte M, Koop G, and Korobilis D (2014) Hierarchical shrinkage in time-varying coefficient models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 41 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 |