  We develop a behavioral asset pricing model in which agents trade in a market
with information friction. Profit-maximizing agents switch between trading
strategies in response to dynamic market conditions. Due to noisy private
information about the fundamental value, the agents form different evaluations
about heterogeneous strategies. We exploit a thin set---a small
sub-population---to pointly identify this nonlinear model, and estimate the
structural parameters using extended method of moments. Based on the estimated
parameters, the model produces return time series that emulate the moments of
the real data. These results are robust across different sample periods and
estimation methods.
