TY - Journal Article
T1 - High-dimensional macroeconomic forecasting using message passing algorithms
A1 - Korobilis, Dimitris
PB - Taylor & Francis
PY - 2021
UR - https://bonnus.ulb.uni-bonn.de/SummonRecord/FETCH-glasgow_eprints_oai_eprints_gla_ac_uk_1968430
N2 - 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.
KW - HA Statistics
ER -