High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

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 pr...

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Vydané v:Journal of business & economic statistics Ročník 39; číslo 2; s. 493 - 504
Hlavný autor: Korobilis, Dimitris
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Alexandria Taylor & Francis 20.03.2021
Taylor & Francis Ltd
Predmet:
Bayesian shrinkage
Belief propagation
Factor graph
High-dimensional inference
Time-varying parameter model
ISSN:0735-0015, 1537-2707
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2019.1677472