class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN DYNAMIC REGRESSION MODELS ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
  
  family : 
      e.g. pf.Normal(0,1)
docstr: Makes dynamic in-sample predictions with the estimated model
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps would you like to forecast?
  
  Returns
  ----------
  - pd.DataFrame with predicted values
docstr_tok: ["Makes","dynamic","in-sample","predictions","with","the","estimated","model","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","would","you","like","to","forecast","?","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict_is(self, h=5):
      """Makes dynamic in-sample predictions with the estimated model
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps would you like to forecast?
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      predictions = []
  
      for t in range(0, h):
          data1 = self.data_original.iloc[: -h + t, :]
          data2 = self.data_original.iloc[-h + t :, :]
          x = NDynReg(
              formula=self.formula, data=data1, family=self.family
          )
          x.fit(print_progress=False)
          if t == 0:
              predictions = x.predict(1, oos_data=data2)
          else:
              predictions = pd.concat(
                  [predictions, x.predict(1, oos_data=data2)]
              )
  
      predictions.rename(columns={0: self.data_name}, inplace=True)
      predictions.index = self.index[-h:]
  
      return predictions
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .dynlin import *
label_desc: Use image node_id 22 for calling the NDynReg obj's underlying member method code with example usage: obj.predict_is(h) and returns: predictions