  The R-package phtt provides estimation procedures for panel data with large
dimensions n, T, and general forms of unobservable heterogeneous effects.
Particularly, the estimation procedures are those of Bai (2009) and Kneip,
Sickles, and Song (2012), which complement one another very well: both models
assume the unobservable heterogeneous effects to have a factor structure. Kneip
et al. (2012) considers the case in which the time varying common factors have
relatively smooth patterns including strongly positive auto-correlated
stationary as well as non-stationary factors, whereas the method of Bai (2009)
focuses on stochastic bounded factors such as ARMA processes. Additionally, the
phtt package provides a wide range of dimensionality criteria in order to
estimate the number of the unobserved factors simultaneously with the remaining
model parameters.
