Nonparametric mixed frequency monitoring macro-at-risk

Massimiliano Marcellino and Michael Pfarrhofer

Economics Letters, 2025, vol. 255, issue C

Abstract: We compare homoskedastic and heteroskedastic mixed frequency (MF) vector autoregression and Bayesian additive regression tree (BART) models to assess their performance in predicting tail risk at short horizons. MF-BART is a nonlinear state space model, and we discuss approximation-based approaches to devise a computationally efficient estimation algorithm. The models are applied in an out-of-sample exercise for quarterly and monthly macroeconomic variables in Italy. The proposed econometric refinements yield improvements in predictive accuracy.

Keywords: Bayesian additive regression trees; Precision sampling; Backcast; Nowcast; Forecast (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 E31 E37 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176525003350
Full text for ScienceDirect subscribers only

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:eee:ecolet:v:255:y:2025:i:c:s0165176525003350

DOI: 10.1016/j.econlet.2025.112498

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().