ramchoice-package: ramchoice: Revealed Preference and Attention Analysis in...

In ramchoice: Revealed Preference and Attention Analysis in Random Limited Attention Models

ramchoice: Revealed Preference and Attention Analysis in Random Limited Attention Models

Description

Information about socio-economic agent's preference (consumer, firm, organization, voter, etc.) is important not only for understanding the decision-making process, but also for conducting welfare analysis and providing robust policy recommendations. However, it is widely documented in psychology, economics and other disciplines that decision makers may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid.

This package implements the estimation and inference procedures documented in Cattaneo, Ma, Masatlioglu, and Suleymanov (2020), and Cattaneo, Cheung, Ma, and Masatlioglu (2022), which utilize standard choice data to partially identify decision maker's preference and attention. For statistical inference, several simulation-based critical values are provided.

The following functions are provided: revealPref and revealAtte (the main functions for revealed preference and attention analysis), sumData, genMat, logitAtte, logitSimu. A simulated dataset ramdata is also included for illustration purposes.

Author(s)

Matias D. Cattaneo, Princeton University. [e-mail].

Paul Cheung, University of Maryland. [e-mail]

Xinwei Ma (maintainer), University of California San Diego. [e-mail]

Yusufcan Masatlioglu, University of Maryland. [e-mail]

Elchin Suleymanov, Purdue University. [e-mail]

References

M. D. Cattaneo, X. Ma, Y. Masatlioglu, and E. Suleymanov (2020). A Random Attention Model. Journal of Political Economy 128(7): 2796-2836. doi: 10.1086/706861

M. D. Cattaneo, P. Cheung, X. Ma, and Y. Masatlioglu (2022). Attention Overload. Working paper.

ramchoice documentation built on May 24, 2022, 1:06 a.m.