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High-dimensional macroeconomic forecasting using message passing algorithms

Dimitris Korobilis

MPRA Paper from University Library of Munich, Germany

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.

Keywords: high-dimensional inference; factor graph; Belief Propagation; Bayesian shrinkage; time-varying parameter model (search for similar items in EconPapers)
JEL-codes: C01 C11 C13 C52 C53 C55 C61 E31 E37 (search for similar items in EconPapers)
Date: 2019-09-15
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
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Related works:
Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019) Downloads
Working Paper: Forecasting with many predictors using message passing algorithms (2017) Downloads
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