  We seek to improve the accuracy of joint galaxy photometric redshift
estimation and spectral energy distribution (SED) fitting. By simulating
different sources of uncorrected systematic errors, we demonstrate that if the
uncertainties on the photometric redshifts are estimated correctly, so are
those on the other SED fitting parameters, such as stellar mass, stellar age,
and dust reddening. Furthermore, we find that if the redshift uncertainties are
over(under)-estimated, the uncertainties in SED parameters tend to be
over(under)-estimated by similar amounts. These results hold even in the
presence of severe systematics and provide, for the first time, a mechanism to
validate the uncertainties on these parameters via comparison with
spectroscopic redshifts. We propose a new technique (annealing) to re-calibrate
the joint uncertainties in the photo-z and SED fitting parameters without
compromising the performance of the SED fitting + photo-z estimation. This
procedure provides a consistent estimation of the multidimensional probability
distribution function in SED fitting + z parameter space, including all
correlations. While the performance of joint SED fitting and photo-z estimation
might be hindered by template incompleteness, we demonstrate that the latter is
"flagged" by a large fraction of outliers in redshift, and that significant
improvements can be achieved by using flexible stellar populations synthesis
models and more realistic star formation histories. In all cases, we find that
the median stellar age is better recovered than the time elapsed from the onset
of star formation [abridged].
