  In fields that are mainly nonexperimental, such as economics and finance, it
is inescapable to compute test statistics and confidence regions that are not
probabilistically independent from previously examined data. The Bayesian and
Neyman-Pearson inference theories are known to be inadequate for such a
practice. We show that these inadequacies also hold m.a.e. (modulo
approximation error). We develop a general econometric theory, called the
neoclassical inference theory, that is immune to this inadequacy m.a.e. The
neoclassical inference theory appears to nest model calibration, and most
econometric practices, whether they are labelled Bayesian or \`a la
Neyman-Pearson. We derive a general, but simple adjustment to make standard
errors account for the approximation error.
