The strategy frequency estimation method (Dal Bó and Fréchette in Am Econ Rev 101(1):411-429, 2011; Fudenberg in Am Econ Rev 102(2):720-749, 2012) allows us to estimate the fraction of subjects playing each of a list of strategies in an infinitely repeated game. Currently, this method assumes that subjects tremble with the same probability. This paper extends this method, so that subjects’ trembles can be heterogeneous. Out of 60 ex ante plausible specifications, the selected model uses the six strategies described in Dal Bó and Fréchette (2018), and allows the distribution of trembles to vary by strategy.

In this paper, motivated by aspects of preregistration plans we discuss issues that we believe have important implications for how experiments are designed. To make possible valid inferences about the effects of a treatment in question, we first illustrate how economic theories can help allocate subjects across treatments in a manner that boosts statistical power. Using data from two laboratory experiments where subject behavior deviated sharply from theory, we show that the ex-post subject allocation to maximize statistical power is closer to these ex-ante calculations relative to traditional designs that balances the number of subjects across treatments. Finally, we call for increased attention to (i) the appropriate levels of the type I and type II errors for power calculations, and (ii) how experimenters consider balance in part by properly handling over-subscription to sessions.

We provide the first, in experimental economics, consistent estimates of a dynamic learning model with a continuous outcome. The econometric approach we propose can be used in many experimental studies including auctions, bargaining with transfers, and gift exchange experiments. We focus on affiliated private value auctions, where subjects are generally assumed to converge to the rule-of-thumb bidding, but our general approach is applicable to many other settings. Our IV estimates suggest that subjects become significantly less aggressive over time; specifically, they decrease their bids in proportion to the previous period’s signal minus bid. However, the inconsistent OLS and FE estimators imply that subjects become significantly more aggressive over time—they raise their bids in proportion to the previous period’s signal minus bid. Our instruments are randomly generated by the experiment, and pass popular weak instrument tests.

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.

Accurately estimating risk preferences is of critical importance when evaluating data from many economic experiments or strategic interactions. I use a simulation model to conduct power analyses over two lottery batteries designed to classify individual subjects as being best explained by one of a number of alternative specifications of risk preference models. I propose a case in which there are only two possible alternatives for classification and find that the statistical methods used to classify subjects result in type I and type II errors at rates far beyond traditionally acceptable levels. These results suggest that subjects in experiments must make significantly more choices, or that traditional lottery pair batteries need to be substantially redesigned to make accurate inferences about the risk preference models that characterize a subject’s choices.

Statistical power is an important detail to consider in the design phase of any experiment. This paper serves as a reference for experimental economists on power calculations. We synthesize many of the questions and issues frequently brought up regarding power calculations and the literature that surrounds that. We provide practical coded examples and tools available for calculating power, and suggest when and how to report power calculations in published studies.

The present study investigates the framing effect of tax–transfer systems on work effort decisions. We devised two theoretically equivalent treatments—the redistributive tax treatment and the redistributive transfer treatment—and studied subjects’ work effort choices in a novel public goods experiment. We found that subjects chose higher effort levels when redistribution took place via transfers than via taxes. Interestingly, the treatment effect was more pronounced among those with lower cognitive abilities and those who judged the tax–transfer system to be unfair. The results have the potential to offer insight on the debate about the extent to which taxes or transfers should be actively used for redistribution.

There is a long-standing unresolved debate in game theory and experimental economics regarding the behavioral equivalence of the direct-response method (hot play) and the strategy method (cold play). Using a unified experimental framework, we compare behavior elicited via both methods in four different Centipede Games that differ in their incentives to take or pass, in the evolution of those incentives over decision nodes, and in the asymmetry of the incentives across the two player roles. Out of the four Centipede Games, we find that both methods yield statistically different behavior in two of them, while in the remaining two we cannot reject the same behavior across the hot and cold methods. Whenever the behavior diverges, hot play consistently makes individuals stop earlier. These findings should shift the question from whether both methods are generically behaviorally equivalent to under which conditions they are (not) and why.