  We propose three methods for forecasting a time series modeled using a
functional coefficient autoregressive model (FCAR) fit via spline-backfitted
local linear (SBLL) smoothing. The three methods are a "naive" plug-in method,
a bootstrap method, and a multistage method. We present asymptotic results of
the SBLL estimation method for FCAR models and show the estimators are oracally
efficient. The three forecasting methods are compared through simulation. We
find that the naive method performs just as well as the multistage method and
even outperforms it in some situations. We apply the naive and multistage
methods to solar irradiance data and compare forecasts based on our method to
those of a linear AR model, the model most commonly applied in the solar energy
literature.
