  Many economic panel and dynamic models, such as rational behavior and Euler
equations, imply that the parameters of interest are identified by conditional
moment restrictions. We introduce a novel inference method without any prior
information about which conditioning instruments are weak or irrelevant.
Building on Bierens (1990), we propose penalized maximum statistics and combine
bootstrap inference with model selection. Our method optimizes asymptotic power
by solving a data-dependent max-min problem for tuning parameter selection.
Extensive Monte Carlo experiments, based on an empirical example, demonstrate
the extent to which our inference procedure is superior to those available in
the literature.
