Estimation of Public Goods Game Data

This paper compares the performance of alternative estimation approaches for Public Goods Game data. A leave-one-out cross validation was applied to test the performance of five estimation approaches. Random effects is revealed as the best estimation approach because of its un-biased and precise estimates and its ability to estimate time-invariant demographics. Surprisingly, approaches that treat the choice variable as continuous out-perform those that treat the choice variable as discrete. Correcting for censoring is shown to induce biased estimates. A finite Poisson mixture model produced relatively un-biased estimates however lacked the precision of fixed and random effects estimation.