TITLE:
Adaptive Learning in Short Time Series
AUTHORS:
Georgios Prokopos, Foteini Kyriazi
KEYWORDS:
Price Forecasting, Adaptive Learning, Quantile Regression, Energy-Agriculture Nexus, Volatility
JOURNAL NAME:
Theoretical Economics Letters,
Vol.15 No.3,
June
12,
2025
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.