Assessments of climate change impacts on agriculture are increasingly relying on panel models to examine the relationship between agricultural outcomes and weather fluctuations. This article reviews the strengths and weaknesses of such models. We argue that panel models are ideal for assessing climate impacts on agriculture because they use group fixed effects to absorb all time-invariant variation and thus rely on weather deviations from the mean that are random and exogenous. Using this random and exogenous source of variation is crucial to identifying a causal relationship between agricultural outcomes and weather. In addition, the large number of observations offered by a panel data set allows the identification of a nonlinear response function, which is an important step in modeling the effects of climate change, as the response can be highly nonlinear. Despite these strengths of panel models, they may still suffer from omitted variable biases of time-varying variables, such as pollution shocks, which are correlated with the weather shocks. Moreover, because group fixed effects absorb a lot of the signal in the weather variables, the signal:noise ratio might decrease. Thus researchers should be careful when constructing the weather variables in order to avoid having noise in the data that causes downward biases in the coefficients.