  Estimation and inference in dynamic discrete choice models often relies on
approximation to lower the computational burden of dynamic programming.
Unfortunately, the use of approximation can impart substantial bias in
estimation and results in invalid confidence sets. We present a method for set
estimation and inference that explicitly accounts for the use of approximation
and is thus valid regardless of the approximation error. We show how one can
account for the error from approximation at low computational cost. Our
methodology allows researchers to assess the estimation error due to the use of
approximation and thus more effectively manage the trade-off between bias and
computational expedience. We provide simulation evidence to demonstrate the
practicality of our approach.
