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Abstract
There is typically an uneasiness among a large class of experimenters in presenting regression
results making full use of the time-series cross-section data from experiments. This uneasiness is
expressed as concern about unaccounted dependencies in the error structure of the regression
model, and in some extreme cases, they argue that each experimental session provides at most a
single observation. The alternative, often recommended in these cases, is to use the
Wilcoxon/Mann-Whitney test using session level mean data. Here discuss the use of panel data
econometrics when there are correlations across subjects. We show that such techniques can deal
with very complicated correlation structures, although we acknowledge that the small sample
properties of allowing for such structures are not well known. We discuss two relatively simple
estimators for less complex cases. In Section 4 we consider the Wilcoxon/Mann-Whitney test and
note that this approach is based on the assumption that session level mean observations are
identically and independently distributed. Thus, the test will be invalid if, for example, there are
different numbers of subjects in experimental sessions (resulting in different variances for the
session means) or there is heteroskedacticity in the underlying data. Finally we compare the
approach of estimating the effect of learning, gender, SAT/ACT score and college major on the
bids of experienced subjects in a common value auction experiment using the panel data
econometrics and the Wilcoxon/Mann-Whitney approaches respectively.
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