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Wu, X., Zhang, Z., Chang, H., & Huang, Q. (2021). A Data-Driven Gross Domestic Product Forecasting Model Based on Multi-Indicator Assessment. IEEE Access, 9, 99495-99503.
https://doi.org/10.1109/access.2021.3062671

has been cited by the following article:

• TITLE: Nonlinear GDP Forecasting: A Threshold-ARDLX Approach with Leading Macroeconomic Indicators

  AUTHORS: George Baimpos, Foteini Kyriazi

  KEYWORDS: Forecasting, GDP Growth, Energy Indices, Leading Indicators

  JOURNAL NAME: Theoretical Economics Letters, Vol.15 No.2, April 18, 2025

  ABSTRACT: Existing research has demonstrated a strong relationship between energy indices and GDP across various economic structures. In this paper, we contribute to this discussion by evaluating the predictive ability of a set of energy and industrial variables to forecast GDP growth through the introduction of leading indicator models. We develop three distinct leading indices that incorporate energy prices, energy consumption, capacity utilization, and industrial production, assessing their effectiveness in capturing future economic trends. To ensure robustness, we use AR(1) and AR(2) models as benchmarks, employing both recursive and rolling window approaches. Our results demonstrate that, in most of the evaluated schemes and rolling window combinations, the proposed models outperform the benchmarks, whether using least squares or quantile regression estimation.