Time-varying sparsity in dynamic regression models

By Maria Kalli, Jim E. Griffin. 2014

83 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/aa037e368693fe47048f725cefeb7ea6d3b07228

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Allan G. Timmermann
2. Graham Elliott
3. Jim E. Griffin
4. Philip J. Brown
5. Antonio Gargano
6. Barbara Rossi
7. Andrew J. Patton
8. Massimo Guidolin

In Management Science and Operations Research

1. Allan G. Timmermann
2. Graham Elliott
3. Philip J. Brown
4. Antonio Gargano
5. Jim E. Griffin
6. Barbara Rossi
7. Andrew J. Patton
8. Nicolas Huck

In Statistics and Probability

1. Philip J. Brown
2. Jim E. Griffin
3. Antonio Gargano
4. Allan G. Timmermann
5. Graham Elliott
6. Gregor Kastner
7. Ulrich K. Müller
8. Mark F. J. Steel

Citations in the corpus

• Complete subset regressions (2013, background): “To deal with this, Groen et al. (2013) consider an approach that accounts for breaks in the individual models’ parameters as well as breaks in the error variance of the overall combination.”
• Hierarchical Shrinkage Priors for Regression Models (2017, methodology): “Reviews of Bayesian analysis of these models are given by Kohn et al. (2001) and Denison et al.”