Comparing alternative estimation methods of a public goods game

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.