Identification and Inference in Limited Attention Models
Friday, Jan. 4, 2019 2:30 PM - 4:30 PM
- Chair: Francesca Molinari, Cornell University
A Random Attention Model
AbstractWe introduce a Random Attention Model (RAM) allowing for a large class of stochastic consideration maps in the context of an otherwise canonical limited attention model for decision theory. The model relies on a new restriction on the unobserved, possibly stochastic consideration map, termed Monotonic Attention, which is intuitive and nests many recent contributions in the literature on limited attention. We develop revealed preference theory within RAM and obtain precise testable implications for observable choice probabilities. Using these results, we show that a set (possibly a singleton) of strict preference orderings compatible with RAM is identifiable from the decision maker's choice probabilities, and establish a representation of this identified set of unobserved preferences as a collection of inequality constrains on her choice probabilities. Given this nonparametric identication result, we develop uniformly valid inference methods for the (partially) identiable preferences. We showcase the performance of our proposed econometric methods using simulations, and provide general-purpose software implementation of our estimation and inference results in the R software package ramchoice . Our proposed econometric methods are computationally very fast to implement.
Inferring Cognitive Heterogeneity from Aggregate Choices
AbstractWe study the problem of identifying the distribution of cognitive characteristics in a population of agents when only aggregate choice behavior from a single menu is observable. Focusing on two models of limited attention, we demonstrate that both “consideration probability” and “consideration capacity” distributions are substantially identified by aggregate choice shares when tastes are homogeneous. We then show how our methodology can be extended to allow for heterogeneous tastes, and suggest how the attention models can be embedded in an econometric specification of the inference problem. Finally, we conduct Monte Carlo simulations of both models and use our results to recover the true parameters.
What Do Consumers Consider Before They Choose? Identification from Asymmetric Demand Responses
AbstractConsideration set models relax the assumption that consumers are aware of all available options. Thus far, identification arguments for these models have relied either on auxiliary data on what options were considered or on instruments excluded from consideration or utility. In a discrete choice framework subsuming logit, probit and random coefficients models, we prove that utility and consideration set probabilities can be separately identified without these data intensive methods. In full-consideration models, choice probabilities satisfy a symmetry property analogous to Slutsky symmetry in continuous choice models. This symmetry breaks down in consideration set models when changes in characteristics perturb consideration, and we show that consideration probabilities are constructively identified from the resulting asymmetries. In a lab experiment, we recover preferences and consideration probabilities using only data on which items were ultimately chosen, and we apply the model to study hotel choices on Expedia.com and insurance choices in Medicare Part D.
- C4 - Econometric and Statistical Methods: Special Topics
- D9 - Micro-Based Behavioral Economics