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) |
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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 Faculty of Social Sciences > Essex Business School Faculty of Social Sciences > Essex Business School > Essex Finance Centre |
SWORD Depositor: | Elements |
Depositing User: | Elements |
Date Deposited: | 28 Apr 2017 10:31 |
Last Modified: | 06 Jan 2022 14:46 |
URI: | http://repository.essex.ac.uk/id/eprint/19565 |
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