Korobilis, D (2017) Forecasting with many predictors using message passing algorithms. Working Paper. Essex Finance Centre Working Papers, Colchester.
|
Text
18_GAMP_DK.pdf Download (752kB) | Preview |
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 |
Actions (login required)
 |
View Item |