High-dimensional macroeconomic forecasting using message passing algorithms

Korobilis, D. (2021) High-dimensional macroeconomic forecasting using message passing algorithms. Journal of Business and Economic Statistics, 39(2), pp. 493-504. (doi: 10.1080/07350015.2019.1677472)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Korompilis Magkas, Professor Dimitris
Authors: Korobilis, D.
Subjects:H Social Sciences > HA Statistics
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Journal of Business and Economic Statistics
Publisher:Taylor & Francis
ISSN (Online):1537-2707
Published Online:22 November 2019
Copyright Holders:Copyright © 2019 American Statistical Association
First Published:First published in Journal of Business and Economic Statistics 39(2): 493-504
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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