Penalized Bayesian Approach-Based Variable Selection for Economic Forecasting

Antonio Pacifico () and Daniela Pilone
Additional contact information
Antonio Pacifico: Department of Economics and Law, University of Macerata, Piazza Strambi 1, 62100 Macerata, Italy
Daniela Pilone: Department of Economics and Finance, LUISS Guido Carli University, Viale Romania 32, 00198 Rome, Italy

JRFM, 2024, vol. 17, issue 2, 1-17

Abstract: This paper proposes a penalized Bayesian computational algorithm as an improvement to the LASSO approach for economic forecasting in multivariate time series. Methodologically, a weighted variable selection procedure is involved in handling high-dimensional and highly correlated data, reduce the dimensionality of the model and parameter space, and then select a promising subset of predictors affecting the outcomes. It is weighted because of two auxiliary penalty terms involved in prior specifications and posterior distributions. The empirical example addresses the issue of pandemic disease prediction and the effects on economic development. It builds on a large set of European and non-European regions to also investigate cross-unit heterogeneity and interdependency. According to the estimation results, density forecasts are conducted to highlight how the promising subset of covariates would help to predict potential contagion due to pandemic diseases. Policy issues are also discussed.

Keywords: time-varying parameters; penalized approaches; machine learning techniques; Bayesian inference; forecasting; disease prediction (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/2/84/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/2/84/ (text/html)

Related works:
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:gam:jjrfmx:v:17:y:2024:i:2:p:84-:d:1340985

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().