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Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach
Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach
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Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach
Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach

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Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach
Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach
Journal Article

Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach

2026
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Overview
IntroductionSince the 1960s, global agricultural prices have exhibited significant episodic volatility. This study aims to conduct a trend-cycle decomposition of these prices to identify the cyclical patterns of their long-run dynamic evolution. Furthermore, it investigates the underlying drivers of long-term price dynamics and their structural transformations over time.MethodsThis study employs two distinct econometric approaches to analyze global agricultural price dynamics. First, we utilize a refined Beveridge–Nelson (BN) filter to decompose global agricultural prices into their trend and cyclical components. Second, to investigate the sources of long-term fluctuations, we disaggregate agricultural commodities into food and non-food sectors and construct a two-sector unobserved components model with stochastic volatility (UC-SV).ResultsThe analysis yields the following key findings: Overall Pattern: Since the 1960s, global agricultural prices have undergone two episodes of rapid growth and three distinct adjustment cycles. Trend-Realization Divergence: Following the turn of the 21st century, global agricultural trend inflation diverged markedly from realized inflation. Structural Shift: Variance decomposition reveals that prior to the 21st century, trend inflation volatility was jointly driven by food prices, non-food prices, and their co-movement. However, post-2000, the contribution from non-food prices diminished, while the share attributable to food prices intensified considerably. Intra-food Dynamics: Further decomposition of food trend inflation variance indicates that the covariance between cereal and non-cereal prices, along with the direct contribution of non-cereals, constitutes a substantial portion of the volatility.DiscussionThe findings reveal a profound transformation in the price formation mechanism of global agricultural commodities. The observed asymmetry between trend and realized inflation suggests that high-frequency, short-term factors now exert a significant and intensifying influence, thereby amplifying the “noise” component in price movements. Moreover, the post-2000 dominance of food prices and the prominence of cross-commodity linkages underscore the critical role of inter-commodity correlations in driving long-run food price volatility.