  It is well known that models used in conventional regression analysis are
commonly misspecified. A standard response is little more than a shrug. Data
analysts invoke Box's maxim that all models are wrong and then proceed as if
the results are useful nevertheless. In this paper, we provide an alternative.
Regression models are treated explicitly as approximations of a true response
surface that can have a number of desirable statistical properties, including
estimates that are asymptotically unbiased. Valid statistical inference
follows. We generalize the formulation to include regression functionals, which
broadens substantially the range of potential applications. An empirical
application is provided to illustrate the paper's key concepts.
