Year: 2025
Type: article
Abstract: This paper applies the novel adaptive learning methodology to forecast agricultural and energy prices in Greece’s volatile, data-scarce markets. We combine traditional ordinary least squares with quantile regression techniques within this framework, achieving up to 27% lower forecast errors compared to conventional benchmarks. Our analysis reveals distinct performance patterns: quantile regression demonstrates superior accuracy for volatile commodities (e.g., barley), while ordinary least squares performs better for stable markets (e.g., maize). The learning rate parameter γ proves crucial in adapting to market conditions. These findings provide policymakers with an enhanced tool for analyzing energy-agriculture price linkages and managing market volatility, particularly in small, open economies facing data limitations.
Source: Theoretical Economics Letters
Authors: Georgios Prokopos, Foteini Kyriazi
Institution: Agricultural University of Athens
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDGs: Zero hunger, Climate action
Open Access status: diamond
OpenAlex ID: W4411248768