Sparse Bayesian Variable Selection with Correlation Prior for Forecasting Macroeconomic Variable using Highly Correlated Predictors

Aijun Yang, Ju Xiang (), Lianjie Shu and Hongqiang Yang ()
Additional contact information
Aijun Yang: Nanjing Forestry University
Ju Xiang: South University of Science and Technology of China
Lianjie Shu: University of Macau

Computational Economics, 2018, vol. 51, issue 2, No 7, 323-338

Abstract: Abstract In this paper, we propose an integrated sparse Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. The variable selection is performed through the stochastic search variable selection technique. We assign a sparse prior distribution on the regression parameters and a correlation prior distribution for the binary vector. The performance of the proposed variable selection method is illustrated in forecasting one major macroeconomic time series of the US economy. Empirical results show that in terms of absolute forecast error and log predictive likelihood, our proposed method performs better than other three methods.

Keywords: Sparse Bayesian variable selection; Correlation prior; Highly correlated predictors; Out-of-sample forecasting (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9741-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:51:y:2018:i:2:d:10.1007_s10614-017-9741-1

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9741-1

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().