High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

Dimitris Korobilis

Journal of Business & Economic Statistics, 2021, vol. 39, issue 2, 493-504

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.

Date: 2021
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Citations: View citations in EconPapers (8)

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Related works:
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2020)
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019)
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019)
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019)
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DOI: 10.1080/07350015.2019.1677472

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