Bayesian MIDAS penalized regressions: estimation, selection, and prediction
Matteo Mogliani ()
Working papers from Banque de France
We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. To improve the sparse recovery ability of the model, we also consider a Group Lasso with a spike-and-slab prior. Penalty hyper-parameters governing the model shrinkage are automatically tuned via an adaptive MCMC algorithm. Simulations show that the proposed models have good selection and forecasting performance, even when the design matrix presents high cross-correlation. When applied to U.S. GDP data, the results suggest that financial variables may have some, although limited, short-term predictive content.
Keywords: MIDAS regressions; penalized regressions; variable selection; forecasting; Bayesian estimation. (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fdg, nep-for, nep-mac and nep-ore
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Working Paper: Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:713
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