High-Dimensional Spatial-Plus-Vertical Price Relationships and Price Transmission: A Machine Learning Approach

TL;DR

This paper introduces a machine learning-enhanced econometric approach to analyze complex, high-dimensional spatial and vertical price relationships in agrifood supply chains, revealing heterogeneous and dynamic price transmission patterns.

Contribution

It develops a regularized regression method to extend classical VECM for large-scale, multi-commodity, multi-region price systems, enabling new insights from high-dimensional data.

Findings

Uncovered significant changes in price relationships before and after a hog disease outbreak.

Visualized complex spatial and vertical price transmission pathways.

Detected heterogeneity and dynamic behavior in price transmission across regions and commodities.

Abstract

Price transmission has been studied extensively in agricultural economics through the lens of spatial and vertical price relationships. Classical time series econometric techniques suffer from the "curse of dimensionality" and are applied almost exclusively to small sets of price series, either prices of one commodity in a few regions or prices of a few commodities in one region. However, an agrifood supply chain usually contains several commodities (e.g., cattle and beef) and spans numerous regions. Failing to jointly examine multi-region, multi-commodity price relationships limits researchers' ability to derive insights from increasingly high-dimensional price datasets of agrifood supply chains. We apply a machine-learning method - specifically, regularized regression - to augment the classical vector error correction model (VECM) and study large spatial-plus-vertical price systems. Leveraging weekly provincial-level data on the piglet-hog-pork supply chain in China, we uncover economically interesting changes in price relationships in the system before and after the outbreak of a major hog disease. To quantify price transmission in the large system, we rely on the spatial-plus-vertical price relationships identified by the regularized VECM to visualize comprehensive spatial and vertical price transmission of hypothetical shocks through joint impulse response functions. Price transmission shows considerable heterogeneity across regions and commodities as the VECM outcomes imply and display different dynamics over time.