Forecasting GDP over the Business Cycle in a Multi‐Frequency and Data‐Rich Environment

Bessec, Marie; Bouabdallah, Othman

doi:10.1111/obes.12069

Cited by 7 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, high-frequency financial data plays a role in the macro forecast. Bessec and Bouabdallah [26] construct an FA-MIDAS model with Markov transformation, in which they also utilize high-frequency financial data to predict U.S. GDP growth. They obtain good in-sample estimates in their analytical framework.…”

Section: Institutional Background and Literature Reviewmentioning

confidence: 99%

On the Predictability of China Macro Indicator with Carbon Emissions Trading

Chen

Gao

Xie³

et al. 2021

Energies

View full text Add to dashboard Cite

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

show abstract

Section: Institutional Background and Literature Reviewmentioning

confidence: 99%