High-dimensional macroeconomic forecasting using message passing algorithms
Working Paper series from Rimini Centre for Economic Analysis
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: C11 C22 C52 C55 C61 (search for similar items in EconPapers)
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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:rim:rimwps:19-17
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