Korobilis, D (2017) Forecasting with many predictors using message passing algorithms. Working Paper. Essex Finance Centre Working Papers, Colchester.

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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.

Item Type:	Monograph (Working Paper)
Uncontrolled Keywords:	high-dimensional inference; compressive sensing; belief propagation; Bayesian shrinkage; dynamic factor models
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
Divisions:	Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User:	Jim Jamieson
Date Deposited:	28 Apr 2017 10:31
Last Modified:	07 Aug 2019 21:15
URI:	http://repository.essex.ac.uk/id/eprint/19565

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