Forecasting with many predictors using message passing algorithms

Dimitris Korobilis

Essex Finance Centre Working Papers from University of Essex, Essex Business School

Abstract: Machine learning methods are becoming increasingly popular in economics, due to the increased availability of large datasets. In this paper I evaluate a recently proposed algorithm called Generalized Approximate Message Passing (GAMP) , which has been very popular in signal processing and compressive sensing. I show how this algorithm can be combined with Bayesian hierarchical shrinkage priors typically used in economic forecasting, resulting in computationally efficient schemes for estimating high-dimensional regression models. Using Monte Carlo simulations I establish that in certain scenarios GAMP can achieve estimation accuracy comparable to traditional Markov chain Monte Carlo methods, at a tiny fraction of the computing time. In a forecasting exercise involving a large set of orthogonal macroeconomic predictors, I show that Bayesian shrinkage estimators based on GAMP perform very well compared to a large set of alternatives.

Keywords: high-dimensional inference; compressive sensing; belief propagation; Bayesian shrinkage; dynamic factor models (search for similar items in EconPapers)
Date: 2017-05-06
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-for
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://repository.essex.ac.uk/19565/ original version (application/pdf)

Related works:
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019)
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:esy:uefcwp:19565

Access Statistics for this paper

More papers in Essex Finance Centre Working Papers from University of Essex, Essex Business School Contact information at EDIRC.
Bibliographic data for series maintained by Nikolaos Vlastakis ().