 Analyzing price efficiency using machine learning generated price indices: The case of the Chilean used car marketFernando 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) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:152:y:2025:i:c:s0264999325002524 DOI: 10.1016/j.econmod.2025.107257 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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