title: ARIMA: forecasting next steps without updating model
body: <p>I would like to forecast a time series as in this example, with an ARIMA of order p=2.</p>
  <pre><code>from statsmodels.tsa.arima.model import ARIMA
  model = ARIMA(series, order=(2,0,0))
  fit = model.fit()
  
  forecast = fit.forecast()[0]
  </code></pre>
  <p>This code <code> fit.forecast()[0]</code> gives the forecast for the next one step, given the last two steps in the series <code>series</code> .
  If I use <code> fit.forecast()[0:n]</code> it will give the forecast for the next n steps.
  We have a series from time 0 to time t and the function forecast the next n steps (t+1, t+2...t+n). Since this is an ARIMA with p=2, every step is forecasted by using the last 2 steps as predictors. With this method, however, the last 2 step are actually the past forecasted values...</p>
  <p>I would like to do a different thing.
  I would like to forecast each step (t+1, t+2...t+n) given the past <strong>actual</strong> values, not <strong>forecasted</strong> values.
  And more importantly, I <strong>don't want to update</strong> the model at every step, i.e. the regression coefficient should be always the same (the ones found by fitting in the range 0 to t)</p>