High-dimensional macroeconomic forecasting using message passing algorithms
MPRA Paper from University Library of Munich, Germany
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)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
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Working Paper: High-dimensional macroeconomic forecasting using message passing algorithms (2019)
Working Paper: Forecasting with many predictors using message passing algorithms (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:96079
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