A Random Attention Model: Identification, Estimation and Testing

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Revealed preference theory is one of the cornerstones of modern economics and other social and behavioral sciences. However, it assumes that decision makers such as households, politicians or firms take all choices into full consideration, which is unlikely in many situations. This project thus aims to improve upon revealed preference theory by allowing decision makers to pay limited attention to their choices. The investigators will combine both theory and econometrics to develop testable theory and empirical implementations of decision-making under limited attention. The results from this project will help policy makers and social scientists to better understand the decision-making process and further develop better policies or interventions in different settings such as labor markets, elections, or industries. The investigators will first develop a random limited attention model, compatible with a large class of different decision-making process. This model will generalize classical revealed preference theory under full attention as well as previously introduced deterministic limited attention frameworks. Based on this model, this project will obtain concrete testable implications, and will employ modern econometrics and statistical techniques to construct inference procedures with good finite samples properties. Nonparametric identification, estimation and inference methods will be studied in detail. As part of this project, methods for eliciting unobserved, heterogeneous preferences in the context of the random limited attention model will be proposed and implemented using real empirical data.