Analyzing price efficiency using machine learning generated price indices: The case of the Chilean used car market
Fernando Lefort and
Fernando Díaz
Economic Modelling, 2025, vol. 152, issue C
Abstract:
This paper examines how the prices of newly imported cars affect the valuation of used vehicles in Chile’s secondary car market, offering a novel perspective on price efficiency within durable goods markets. Previous studies analyze substitution effects between new and used vehicles in the context of equilibrium models with demand-side heterogeneity. Leveraging a dataset of 2.7 million used car advertisements, we employ Machine Learning techniques to construct synthetic price indices, which serve as the foundation for an event study. Our findings reveal a prompt and statistically significant adjustment in used car prices, particularly among newer and higher-end models, even before the public release of import price data. These results suggest a high degree of informational efficiency in Chile’s used car market and are consistent with demand substitution effects between new and used cars and the incorporation of supply-side shocks by market participants into price valuations.
Keywords: Random Forest; Event study; Market efficiency; Durable goods; Secondary markets (search for similar items in EconPapers)
JEL-codes: C14 C53 C55 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:152:y:2025:i:c:s0264999325002524
DOI: 10.1016/j.econmod.2025.107257
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