High-dimensional mixed data sampling models with a covariate-dependent threshold

Lixiong Yang, Luyao Ren and Yihang Ye

Econometric Reviews, 2025, vol. 44, issue 8, 1079-1119

Abstract: This article introduces high-dimensional mixed data sampling models with a covariate-dependent threshold, which allows for a threshold effect in the relationship between dependent and independent variables sampled at different frequencies and allows for threshold regimes depending on a time-varying threshold being modeled as a linear function of informative covariates. Based on the MCMC technique and linear approximation, we develop a sparse group LASSO (sg-LASSO) estimator of model parameters. We also establish non asymptotic oracle inequalities for the prediction risk, the l1 and l∞ bounds for the parameter estimator and show that these bounds can be translated easily into asymptotic consistency for prediction, estimation, variable selection, and threshold detection. Monte Carlo simulations are conducted to examine the estimation and predictive performance. The simulation results point out that the estimation, prediction, and selection procedures work well in finite samples. The model is illustrated with an empirical application to nowcasting US GDP growth. Our empirical results demonstrate that the proposed model achieves statistically significant gains in GDP nowcasting accuracy by incorporating a threshold effect to capture non linear effects and performing a regime-specific variable selection.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2025.2481471 (text/html)
Access to full text is restricted to subscribers.

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:taf:emetrv:v:44:y:2025:i:8:p:1079-1119

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2025.2481471

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().