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TOPIC:
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ATTENTION OVERLOAD
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ABSTRACT
We introduce an Attention Overload Model that captures the idea that alternatives compete for the decision maker’s attention, and hence the attention frequency each alternative receives decreases as the choice problem becomes larger. Using this nonparametric restriction on the random attention formation, we show that a fruitful revealed preference theory can be developed, and provide testable implications on the observed choice behavior that can be used to partially identify the decision maker’s preference. Furthermore, we provide novel partial identification results on the underlying attention frequency, thereby offering the first nonparametric identification result of (a feature of) the random attention formation mechanism in the literature. Building on our partial identification results, for both preferences and attention frequency, we develop econometric methods for estimation and inference. Importantly, our econometric procedures remain valid even in settings with large number of alternatives and choice problems, an important feature of the economic environment we consider. We also provide a software package in R implementing our empirical methods, and illustrate them in a simulation study.
Keywords: attention frequency, limited and random attention, revealed preference, partial identification, high-dimensional inference.
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Click here to view the speaker's profile.
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This seminar will be held virtually via Zoom. A confirmation email with the Zoom details will be sent to the registered email by 11 March 2022.
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PRESENTER
Xinwei Ma
University of California San Diego
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RESEARCH INTERESTS
Econometric Theory
Causal Inference
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DATE:
14 March 2022 (Monday)
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TIME:
8.30am - 10.00am
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