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Bayesian dynamic variable selection in high dimensions

Gary Koop, Dimitris Korobilis

arXiv 9 Sep 2018 · Statistics — Computation · publishedInternational Economic Review (2022) · 41 citations (OpenAlex)

arXiv:1809.03031 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large number of predictors. This strategy allows for assessing in individual time periods which predictors are relevant (or not) for forecasting the dependent variable. The new algorithm is evaluated numerically using synthetic data and its computational advantages are established. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts of price inflation over a number of alternative forecasting models.

Citation extraction

44
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in-text mentions
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distinct cited
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appendix boundary found by appendix_titled_section at “Data Appendix” · 77% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1George, E. I. and R. E. McCulloch (1993) Variable Selection via Gibbs Sampling1.00073100%
2Rocková, V. and K. McAlinn (2017) Dynamic Variable Selection with Spike-and-Slab Process Priors, Tech1.00065100%
3Koop, G. and D. Korobilis (2012) Forecasting Inflation Using Dynamic Model Averaging self1.00063100%
4Stock, J. H. and M. W. Watson (2007) Why Has U.S1.00053100%
5Zou, H. and T. Hastie (2005) Regularization and Variable Selection via the Elastic Net0.81142100%
6Breiman, L (1996) Bagging Predictors0.73732100%
7Chan, J. and I. Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models0.73732100%
8Sm'dl, V. and A. Quinn (2006) The Variational Bayes Method in Signal Processing0.64441100%
9McCracken, M. and S. Ng (2020) FRED-QD: A Quarterly Database for Macroeconomic Research, Working Paper 26872, National Bureau of Economic Research0.6443267%
10Belmonte, M. A., G. Koop, and D. Korobilis (2014) Hierarchical Shrinkage in Time-Varying Parameter Models0.64422100%

Showing the top 10 of 44 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Variational Bayes in State Space Models: Inferential and Predictive Accuracy0.92853
2Bayesian Approaches to Shrinkage and Sparse Estimation large A guide for applied econometricians large0.81142
3Mixing it up: Inflation at risk0.51132
4Bayesian Forecasting in Economics and Finance: A Modern Review0.51122
5Probabilistic Quantile Factor Analysis\@thefnmark\@footnotetextThe authors gratefully acknowledge helpful comments from participants of the 2023 SNDE symposium and the IAAE 2023 in Oslo. This paper should not be reported as representing the views of Norges Bank. The views expressed are those of the authors and do not necessarily reflect those of Norges Bank. The authors report there are no competing interests to declare0.40511
6Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions0.40511
7Bayesian Nonlinear Regression using Sums of Simple Functions0.40511
82411.094520.40511
9myblue Vector Copula Variational Inference and Dependent Block Posterior Approximations0.40511