Identification and management of nonsystematic cross-commodity data: Toward best practice

Area of Science:

• Behavioral economics
• Econometrics

Background:

• Assessing data systematicity is crucial for understanding behavioral economic demand.
• Existing criteria (Stein et al., 2015) apply only to own-price demand, not cross-price demand.
• Cross-price demand analysis is vital for understanding inter-commodity consumption relationships.

Purpose of the Study:

• To extend existing criteria and algorithms for assessing the systematicity of cross-price demand.
• To develop a method for classifying cross-commodity relationships as substitute, complement, or independent.
• To evaluate the systematicity of cross-commodity demand based on its classification.

Main Methods:

• Adapted Stein et al.'s (2015) criteria and algorithm for cross-price demand.
• Classified cross-commodity demand data into substitute, complement, or independent categories.
• Applied the extended algorithm to three distinct cross-commodity demand datasets.

Main Results:

• Successfully extended the systematicity assessment to cross-price demand.
• Demonstrated the classification of cross-commodities into substitute, complement, or independent relationships.
• Highlighted the importance of data exclusion criteria to avoid bias in demand analysis.

Conclusions:

• The developed algorithm provides a framework for evaluating cross-commodity demand systematicity.
• This advancement allows for more robust analysis of interdependencies between commodities.
• Proper data handling is essential for accurate own-price and cross-price demand modeling.