Time Will Tell: Recovering Preferences when Choices Are Noisy

Carlos Alós-Ferrer, Ernst Fehr and Nick Netzer

No 7333, CESifo Working Paper Series from CESifo

Abstract: The ability to uncover preferences from choices is fundamental for both positive economics and welfare analysis. Overwhelming evidence shows that choice is stochastic, which has given rise to random utility models as the dominant paradigm in applied microeconomics. However, as is well known, it is not possible to infer the structure of preferences in the absence of assumptions on the structure of noise. This makes it impossible to empirically test the structure of noise independently from the structure of preferences. Here, we show that the difficulty can be bypassed if data sets are enlarged to include response times. A simple condition on response time distributions (a weaker version of first-order stochastic dominance) ensures that choices reveal preferences without assumptions on the structure of utility noise. Sharper results are obtained if the analysis is restricted to specific classes of models. Under symmetric noise, response times allow to uncover preferences for choice pairs outside the data set, and if noise is Fechnerian, even choice probabilities can be forecast out of sample. We conclude by showing that standard random utility models from economics and standard drift-diffusion models from psychology necessarily generate data sets fulfilling our sufficient condition on response time distributions.

Keywords: revealed preference; random utility models; response times (search for similar items in EconPapers)
JEL-codes: D11 D81 D83 D87 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ore and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Working Paper: Time will tell - Recovering Preferences when Choices are Noisy (2018)
Working Paper: Time Will Tell: Recovering Preferences When Choices Are Noisy (2018)
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