Bayesian MIDAS penalized regressions: Estimation, selection, and prediction

Matteo Mogliani and Anna Simoni

Journal of Econometrics, 2021, vol. 222, issue 1, 833-860

Abstract: We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian estimation and inference. In particular, to improve the prediction properties of the model and its sparse recovery ability, we 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. We establish good frequentist asymptotic properties of the posterior prediction error, we recover the optimal posterior contraction rate, and we show optimality of the posterior predictive density. Simulations show that the proposed models have good selection and forecasting performance in small samples, even when the design matrix presents cross-correlation. When applied to forecasting U.S. GDP, our penalized regressions can outperform many strong competitors. Results suggest that financial variables may have some, although very limited, short-term predictive content.

Keywords: Bayesian MIDAS regressions; Penalized regressions; Predictive distribution; Forecasting; Posterior contraction (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 E37 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407620302207
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction (2020)
Working Paper: Bayesian MIDAS penalized regressions: Estimation, selection, and prediction (2020)
Working Paper: Bayesian MIDAS penalized regressions: estimation, selection, and prediction (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:833-860

DOI: 10.1016/j.jeconom.2020.07.022

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().