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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Abstract
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 |
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Status: | Published |
Refereed: | Yes |
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: | 0735-0015 |
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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