old_contents: import scikits.statsmodels.base.model as base
  import scikits.statsmodels.base.wrapper as wrap
  
  #TODO: how to handle the docs with the additional parameters?
  class TimeSeriesModel(base.LikelihoodModel):
      def __init__(self, endog, exog=None, dates=None):
          #        self._data = _data = smdata.handle_data(endog, exog)
          #self.exog = _data.exog
          #self.endog = _data.endog
          super(TimeSeriesModel, self).__init__(endog, exog)
          self.dates = dates or self._data.row_labels
  
  #NOTE: this is just a stub for now, overwrite what we need to higher up
  # and bring methods from child classes up as well.
  class TimeSeriesModelResults(base.LikelihoodModelResults):
      def __init__(self, model, params, normalized_cov_params, scale=1.):
          self.dates = model.dates
          super(TimeSeriesModelResults,
                  self).__init__(model, params, normalized_cov_params, scale)
  
  class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
      _attrs = {}
      _wrap_attrs = base.LikelihoodResultsWrapper._attrs.update(_attrs)
      _methods = {}
      _wrap_methods = base.LikelihoodResultsWrapper._wrap_methods.update(methods)
  wrap.populate_wrapper(TimeSeriesResultsWrapper,
                        TimeSeriesModelResults)
  
  if __name__ == "__main__":
      import scikits.statsmodels.api as sm
      import datetime
      import pandas
  
      data = sm.datasets.macrodata.load()
  
      #make a DataFrame
      #TODO: attach a DataFrame to some of the datasets, for quicker use
      dates = [str(int(x[0])) +':'+ str(int(x[1])) \
               for x in data.data[['year','quarter']]]
      try:
          import scikits.timeseries as ts
          ts_dates = ts.date_array(start_date = ts.Date(year=1959,quarter=1,freq='Q'),
                               length=len(data.data))
      except:
          pass
  
  
      df = pandas.DataFrame(data.data[['realgdp','realinv','realcons']], index=dates)
      ex_mod = TimeSeriesModel(df)
      #ts_series = pandas.TimeSeries()
  
  
  
new_contents: import scikits.statsmodels.base.model as base
  import scikits.statsmodels.base.wrapper as wrap
  
  #TODO: how to handle the docs with the additional parameters?
  class TimeSeriesModel(base.LikelihoodModel):
      def __init__(self, endog, exog=None, dates=None):
          #        self._data = _data = smdata.handle_data(endog, exog)
          #self.exog = _data.exog
          #self.endog = _data.endog
          super(TimeSeriesModel, self).__init__(endog, exog)
          self.dates = dates or self._data.row_labels
  
  #NOTE: this is just a stub for now, overwrite what we need to higher up
  # and bring methods from child classes up as well.
  class TimeSeriesModelResults(base.LikelihoodModelResults):
      def __init__(self, model, params, normalized_cov_params, scale=1.):
          self.dates = model.dates
          super(TimeSeriesModelResults,
                  self).__init__(model, params, normalized_cov_params, scale)
  
  class TimeSeriesResultsWrapper(wrap.ResultsWrapper):
      _attrs = {}
      _wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._attrs,
                                      _attrs)
      _methods = {}
      _wrap_methods = wrap.union_dicts(base.LikelihoodResultsWrapper._wrap_methods,
                                       _methods)
  wrap.populate_wrapper(TimeSeriesResultsWrapper,
                        TimeSeriesModelResults)
  
  if __name__ == "__main__":
      import scikits.statsmodels.api as sm
      import datetime
      import pandas
  
      data = sm.datasets.macrodata.load()
  
      #make a DataFrame
      #TODO: attach a DataFrame to some of the datasets, for quicker use
      dates = [str(int(x[0])) +':'+ str(int(x[1])) \
               for x in data.data[['year','quarter']]]
      try:
          import scikits.timeseries as ts
          ts_dates = ts.date_array(start_date = ts.Date(year=1959,quarter=1,freq='Q'),
                               length=len(data.data))
      except:
          pass
  
  
      df = pandas.DataFrame(data.data[['realgdp','realinv','realcons']], index=dates)
      ex_mod = TimeSeriesModel(df)
      #ts_series = pandas.TimeSeries()
  
  
  
subject: Revert to union_dict for updating dicts