A time-varying diffusion index forecasting model

Jie Wei and Yonghui Zhang

Economics Letters, 2020, vol. 193, issue C

Abstract: This paper introduces a novel forecasting method based on a time-varying diffusion index model, where both factor loadings and regression coefficients are allowed to be time-varying. We first obtain the local principal component analysis (PCA) estimators for the latent factors and then estimate the factor augmented forecasting regression with time-varying coefficients nonparametrically. A feasible forecast is proposed by combining the estimated factors and the nonparametric estimators of coefficients. A set of Monte Carlo simulations demonstrates better performance of our proposed method than the standard diffusion index forecasters based on rolling windows. An empirical application of forecasting US macroeconomic variables is provided.

Keywords: Diffusion index; Factor model; Local PCA; Time-varying parameter (search for similar items in EconPapers)
JEL-codes: C23 C55 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176520302172
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:193:y:2020:i:c:s0165176520302172

DOI: 10.1016/j.econlet.2020.109337

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 ().