  Inference on common parameters in panel data models with individual-specific
fixed effects is a classic example of Neyman and Scott's (1948) incidental
parameter problem (IPP). One solution to this IPP is functional differencing
(Bonhomme 2012), which works when the number of time periods T is fixed (and
may be small), but this solution is not applicable to all panel data models of
interest. Another solution, which applies to a larger class of models, is
"large-T" bias correction (pioneered by Hahn and Kuersteiner 2002 and Hahn and
Newey 2004), but this is only guaranteed to work well when T is sufficiently
large. This paper provides a unified approach that connects those two seemingly
disparate solutions to the IPP. In doing so, we provide an approximate version
of functional differencing, that is, an approximate solution to the IPP that is
applicable to a large class of panel data models even when T is relatively
small.
