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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Published in | Journal of business & economic statistics Vol. 39; no. 2; pp. 493 - 504 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Alexandria
Taylor & Francis
20.03.2021
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
ISSN | 0735-0015 1537-2707 |
DOI | 10.1080/07350015.2019.1677472 |
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Summary: | 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.
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for this article are available online. |
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Bibliography: | 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 |