Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning
Syed Badruddoza (),
Modhurima Amin and
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Syed Badruddoza: Washington State University
No 2019-5, Working Papers from School of Economic Sciences, Washington State University
Firms can prioritize among the product attributes based on consumer valuations using market-level data. However, a structural estimation of market demand is challenging, especially when the data are updating in real-time and instrumental variables are scarce. We find evidence that Random Forests (RF)—a machine-learning algorithm—can detect consumers’ sensitivity to product attributes similar to the structural framework of Berry-Levinsohn-Pakes (BLP). Sensitivity to an attribute is measured by the absolute value of its coefficient. We check the RF’s capacity to rank the attributes when prices are endogenous, coefficients are random, and instrumental or demographic variables are unavailable. In our simulations, the BLP estimates correlate with the RF importance factor in ranking (68%) and magnitude (79%), and the rates increase with the sample size. Consumer sensitivity to endogenous variables (price) and variables with random coefficients are overestimated by the RF approach, but ranking of variables with non-random coefficients match with BLP’s coefficients in 96% cases. These estimates are pessimistically derived by RF without parameter-tuning. We conclude that machine-learning does not replace the structural framework but provides firms with a sensible idea of consumers’ ranking of product attributes.
Keywords: Machine-Learning; Random Forests; Demand Estimation; BLP; Discrete Choice. (search for similar items in EconPapers)
JEL-codes: C55 D11 Q11 (search for similar items in EconPapers)
Pages: 24 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com and nep-dcm
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