Code and Data for: Weighted Linear Discrete Choice

Principal Investigator(s):  View help for Principal Investigator(s) Christopher Chambers, Georgetown University; Yusufcan Masatlioglu, University of Maryland; Paulo Natenzon, Washington University in St. Louis; Collin Raymond, Cornell University

Version:  View help for Version V1

Name File Type Size Last Modified
Datasets-2NotPolarizing-3NotPolarizing.csv text/csv 463.6 KB 01/07/2025 03:43:AM
Datasets-2NotPolarizing-3Polarizing.csv text/csv 512.4 KB 01/07/2025 03:43:AM
Datasets-2Polarizing-3NotPolarizing.csv text/csv 512.7 KB 01/07/2025 03:43:AM
Datasets-2Polarizing-3Polarizing.csv text/csv 463.5 KB 01/07/2025 04:12:PM
README.pdf application/pdf 161.3 KB 03/24/2025 05:28:PM
WL-Simulations-Errors-Fit-2-3-Predict-4.m application/mathematica 3.2 MB 10/28/2023 08:31:AM
WL-Simulations-Errors-Fit-2-4-Predict-3.m application/mathematica 3.2 MB 12/10/2023 06:30:AM
WL-Simulations-Errors-Fit-3-4-Predict-2.m application/mathematica 3.2 MB 01/07/2025 03:43:AM
compute-prediction-error-metrics.nb application/mathematica 31.9 KB 03/24/2025 05:14:PM
create-synthetic-data.nb application/mathematica 48 KB 03/24/2025 05:30:PM

Project Citation: 

Chambers, Christopher, Masatlioglu, Yusufcan, Natenzon, Paulo, and Raymond, Collin. Code and Data for: Weighted Linear Discrete Choice. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-04-08. https://doi.org/10.3886/E214961V1

Project Description

Summary:  View help for Summary Data and Mathematica code for "Weighted Linear Discrete Choice"

Abstract: We introduce a new model of stochastic choice that assigns each choice option a utility, along with a salience parameter reflecting economic frictions. We characterize our model behaviorally and investigate its comparative statics properties. We show that the model generates intuitive closed-form solutions in equilibrium settings where firms can choose price, quality, and advertising. In addition, we show that the model allows for flexible substitution patterns and changes in market shares across choice sets. We demonstrate that our model can be easily identified and can outperform alternatives in demand prediction.

Scope of Project

Subject Terms:  View help for Subject Terms Market share; Demand; Aggregate demand; Market demand
JEL Classification:  View help for JEL Classification
      D11 Consumer Economics: Theory
      D21 Firm Behavior: Theory
      D43 Market Structure, Pricing, and Design: Oligopoly and Other Forms of Market Imperfection
      M37 Advertising
Universe:  View help for Universe Consumers in a simulated market economy.
Data Type(s):  View help for Data Type(s) aggregate data; other
Collection Notes:  View help for Collection Notes Simulated demand data.

Methodology

Response Rate:  View help for Response Rate Simulated data
Sampling:  View help for Sampling Simulated data
Data Source:  View help for Data Source Simulated data
Collection Mode(s):  View help for Collection Mode(s) other
Unit(s) of Observation:  View help for Unit(s) of Observation Market
Geographic Unit:  View help for Geographic Unit Global

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Published Versions

V1 [2025-04-08]

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