High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

89 Pages Posted: 15 Nov 2018 Last revised: 18 Sep 2019

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: June 21, 2019


This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either towards zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a Generalized Approximate Message Passing (GAMP) algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well.

Keywords: High-Dimensional Inference, Factor Graph, Belief Propagation, Bayesian Shrinkage, Time-Varying Parameter Model

JEL Classification: C11, C22, C52, C55, C61

Suggested Citation

Korobilis, Dimitris, High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms (June 21, 2019). Available at SSRN: https://ssrn.com/abstract=3271976 or http://dx.doi.org/10.2139/ssrn.3271976

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://https://sites.google.com/site/dimitriskorobilis/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics