old_contents: import statsmodels.api as sm
  import numpy as np
  import pandas
  
  # Getting started
  # ---------------
  
  data = sm.datasets.sunspots.load()
  
  # Right now an annual date series must be datetimes at the end of the year.
  # We can use scikits.timeseries and datetime to create this array.
  
  import datetime
  import scikits.timeseries as ts
  dates = ts.date_array(start_date=1700, length=len(data.endog), freq='A')
  
  # To make an array of datetime types, we need an integer array of ordinals
  
  #.. from datetime import datetime
  #.. dt_dates = dates.toordinal().astype(int)
  #.. dt_dates = np.asarray([datetime.fromordinal(i) for i in dt_dates])
  dt_dates = dates.tolist()
  
  # Using Pandas
  # ------------
  
  # Make a pandas TimeSeries or DataFrame
  endog = pandas.Series(data.endog, index=dt_dates)
  
  # and instantiate the model
  ar_model = sm.tsa.AR(endog, freq='A')
  pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
  
  # Let's do some out-of-sample prediction
  pred = pandas_ar_res.predict(start='2005', end='2015')
  print pred
  
  # Using explicit dates
  # --------------------
  
  ar_model = sm.tsa.AR(data.endog, dates=dt_dates, freq='A')
  ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
  pred = ar_res.predict(start='2005', end='2015')
  print pred
  
  # This just returns a regular array, but since the model has date information
  # attached, you can get the prediction dates in a roundabout way.
  
  print ar_res._data.predict_dates
  
  # This attribute only exists if predict has been called. It holds the dates
  # associated with the last call to predict.
  #..TODO: should this be attached to the results instance?
  
  # Using scikits.timeseries
  # ------------------------
  
  ts_data = ts.time_series(data.endog, dates=dates)
  ts_ar_model = sm.tsa.AR(ts_data, freq='A')
  ts_ar_res = ts_ar_model.fit(maxlag=9)
  
  # Using Larry
  # -----------
  
  import la
  larr = la.larry(data.endog, [dt_dates])
  la_ar_model = sm.tsa.AR(larr, freq='A')
  la_ar_res = la_ar_model.fit(maxlag=9)
  
new_contents: """
  Using dates with timeseries models
  """
  import statsmodels.api as sm
  import scikits.statsmodels.api as sm
  import numpy as np
  import pandas
  
  # Getting started
  # ---------------
  
  data = sm.datasets.sunspots.load()
  
  # Right now an annual date series must be datetimes at the end of the year.
  # We can use scikits.timeseries and datetime to create this array.
  
  import datetime
  import scikits.timeseries as ts
  dates = ts.date_array(start_date=1700, length=len(data.endog), freq='A')
  
  # To make an array of datetime types, we need an integer array of ordinals
  
  #.. from datetime import datetime
  #.. dt_dates = dates.toordinal().astype(int)
  #.. dt_dates = np.asarray([datetime.fromordinal(i) for i in dt_dates])
  dt_dates = dates.tolist()
  
  # Using Pandas
  # ------------
  
  # Make a pandas TimeSeries or DataFrame
  endog = pandas.Series(data.endog, index=dt_dates)
  
  # and instantiate the model
  ar_model = sm.tsa.AR(endog, freq='A')
  pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
  
  # Let's do some out-of-sample prediction
  pred = pandas_ar_res.predict(start='2005', end='2015')
  print pred
  
  # Using explicit dates
  # --------------------
  
  ar_model = sm.tsa.AR(data.endog, dates=dt_dates, freq='A')
  ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
  pred = ar_res.predict(start='2005', end='2015')
  print pred
  
  # This just returns a regular array, but since the model has date information
  # attached, you can get the prediction dates in a roundabout way.
  
  print ar_res._data.predict_dates
  
  # This attribute only exists if predict has been called. It holds the dates
  # associated with the last call to predict.
  #..TODO: should this be attached to the results instance?
  
  # Using scikits.timeseries
  # ------------------------
  
  ts_data = ts.time_series(data.endog, dates=dates)
  ts_ar_model = sm.tsa.AR(ts_data, freq='A')
  ts_ar_res = ts_ar_model.fit(maxlag=9)
  
  # Using Larry
  # -----------
  
  import la
  larr = la.larry(data.endog, [dt_dates])
  la_ar_model = sm.tsa.AR(larr, freq='A')
  la_ar_res = la_ar_model.fit(maxlag=9)
  
subject: Add docstring to example