@Data Set{Summon-FETCH-datacite_primary_10_6084_m9_figshare_9971264_v23,
title = {High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms},
author = {Korobilis, Dimitris},
publisher = {Taylor & Francis},
year = {2019},
keywords ={Medicine, Biotechnology, FOS: Computer and information sciences, Molecular Biology, FOS: Earth and related environmental sciences, FOS: Mathematics, Information Systems not elsewhere classified, Environmental Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified},
abstract = {This article 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 toward 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 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. Supplementary materials for this article are available online.
},
}