Boosting nonlinear predictability of macroeconomic time series

Heikki Kauppi and Timo Virtanen

International Journal of Forecasting, 2021, vol. 37, issue 1, 151-170

Abstract: We apply the boosting estimation method in order to investigate to what extent and at what horizons macroeconomic time series have nonlinear predictability that comes from their own history. Our results indicate that the U.S. macroeconomic time series have more exploitable nonlinear predictability than previous studies have found. On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals.

Keywords: Boosting; Forecasting; Linear autoregression; Macroeconomic time series; Mean squared error; Nonlinearity (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207020300571
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:intfor:v:37:y:2021:i:1:p:151-170

DOI: 10.1016/j.ijforecast.2020.03.008

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

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