Forecasting with Many Predictors Using Message Passing Algorithms

50 Pages Posted: 1 Jun 2017

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: May 6, 2017


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 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

JEL Classification: C11, C13, C15, C22, C52, C53, C61

Suggested Citation

Korobilis, Dimitris, Forecasting with Many Predictors Using Message Passing Algorithms (May 6, 2017). Available at SSRN: or

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics