  This paper develops a method to construct uniform confidence bands in
deconvolution when the error distribution is unknown. We mainly focus on the
baseline setting where an auxiliary sample from the error distribution is
available and the error density is ordinary smooth. The auxiliary sample may
directly come from validation data, or can be constructed from panel data with
a symmetric error distribution. We also present extensions of the results on
confidence bands to the case of super-smooth error densities. Simulation
studies demonstrate the performance of the multiplier bootstrap confidence band
in the finite sample. We apply our method to the Outer Continental Shelf (OCS)
Auction Data and draw confidence bands for the density of common values of
mineral rights on oil and gas tracts. Finally, we present an application of our
main theoretical result specifically to additive fixed-effect panel data
models. As an empirical illustration of the panel data analysis, we draw
confidence bands for the density of the total factor productivity in a
manufacturing industry in Chile.
