High-dimensional macroeconomic forecasting using message passing algorithms

Dimitris Korobilis

Papers from arXiv.org

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.

Date: 2020-04
New Economics Papers: this item is included in nep-gen and nep-ore
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Citations: View citations in EconPapers (4)

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http://arxiv.org/pdf/2004.11485 Latest version (application/pdf)

Related works:
Journal Article: High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms (2021)
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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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2004.11485

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