  Aggregate shocks affect most households' and firms' decisions. Using three
stylized models we show that inference based on cross-sectional data alone
generally fails to correctly account for decision making of rational agents
facing aggregate uncertainty. We propose an econometric framework that
overcomes these problems by explicitly parameterizing the agents' inference
problem relative to aggregate shocks. Our framework and examples illustrate
that the cross-sectional and time-series aspects of the model are often
interdependent. Therefore, estimation of model parameters in the presence of
aggregate shocks requires the combined use of cross-sectional and time series
data. We provide easy-to-use formulas for test statistics and confidence
intervals that account for the interaction between the cross-sectional and
time-series variation. Lastly, we perform Monte Carlo simulations that
highlight the properties of the proposed method and the risks of not properly
accounting for the presence of aggregate shocks.
