  We explore methods to improve the estimates of star formation rates and mean
stellar population ages from broadband photometry of high redshift star-forming
galaxies. We use synthetic spectral templates with a variety of simple
parametric star formation histories to fit broadband spectral energy
distributions. These parametric models are used to infer ages, star formation
rates and stellar masses for a mock data set drawn from a hierarchical
semi-analytic model of galaxy evolution. Traditional parametric models
generally assume an exponentially declining rate of star-formation after an
initial instantaneous rise. Our results show that star formation histories with
a much more gradual rise in the star formation rate are likely to be better
templates, and are likely to give better overall estimates of the age
distribution and star formation rate distribution of Lyman break galaxies. For
B- and V-dropouts, we find the best simple parametric model to be one where the
star formation rate increases linearly with time. The exponentially-declining
model overpredicts the age by 100 % and 120 % for B- and V-dropouts, on
average, while for a linearly-increasing model, the age is overpredicted by 9 %
and 16 %, respectively. Similarly, the exponential model underpredicts
star-formation rates by 56 % and 60 %, while the linearly-increasing model
underpredicts by 15 % 22 %, respectively. For U-dropouts, the models where the
star-formation rate has a peak (near z ~ 3) provide the best match for age --
overprediction is reduced from 110 % to 26 % -- and star-formation rate --
underprediction is reduced from 58 % to 22 %. We classify different types of
star-formation histories in the semi-analytic models and show how the biases
behave for the different classes. We also provide two-band calibration formulae
for stellar mass and star formation rate estimations.
