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Comparing alternative estimation methods of a public goods game

Published online by Cambridge University Press:  01 January 2025

Danielle Kent Open the ORCID record for Danielle Kent [Opens in a new window]
Danielle Kent*
Affiliation:
Department of Economics, Macquarie University, Sydney, NSW 2109, Australia
*
e-mail: danielle.kent@mq.edu.au
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Abstract

This paper empirically compares the use of straightforward verses more complex methods to estimate public goods game data. Five different estimation methods were compared holding the dependent and explanatory variables constant. The models were evaluated using a large out-of-sample cross-country public goods game data set. The ordered probit and tobit random-effects models yielded lower p values compared to more straightforward models: ordinary least squares, fixed and random effects. However, the more complex models also had a greater predictive bias. The straightforward models performed better than expected. Despite their limitations, they produced unbiased predictions for both the in-sample and out-of-sample data.

Keywords

Public goods gamesEconomic experimentsFixed effectsTobit random effectsOrdered probit

JEL classification

C13: Estimation: General H41: Public Goods C92: Laboratory, Group Behavior C81: Methodology for Collecting, Estimating, and Organizing Microeconomic Data • Data Access
Type
Original Paper
Information
Journal of the Economic Science Association , Volume 6 , Issue 2: Special Issue: Statistical Issues for Experimental Economists , December 2020 , pp. 156 - 167
Copyright
Copyright © Economic Science Association 2020

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