  We present a robust method to estimate the redshift of galaxies using
Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed
by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of
2313724 galaxies for which the spectroscopic redshift is obtained from SDSS,
and magnitudes and colours are obtained from the Pan-STARRS1 Data Release 2
survey. The photometric redshift of a galaxy is then estimated by means of a
local linear regression in a 5-dimensional magnitude and colour space. Our
method achieves an average bias of $\overline{\Delta z_{\rm norm}}=-2.01 \times
10^{-4}$, a standard deviation of $\sigma(\Delta z_{\rm norm})=0.0298$, and an
outlier rate of $P_o=4.32\%$ when cross-validating on the training set. Even
though the relation between each of the Pan-STARRS1 colours and the
spectroscopic redshifts is noisier than for SDSS colours, the results obtained
by our method are very close to those yielded by SDSS data. The proposed method
has the additional advantage of allowing the estimation of photometric
redshifts on a larger portion of the sky ($\sim 3/4$ vs $\sim 1/3$). The
training set and the code implementing this method are publicly available at
www.testaddress.com.
