class_docstr: Inherits time series methods from TSM class.
  
  **** VECTOR AUTOREGRESSION (VAR) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  lags : int
      Field to specify how many lag terms the model will have. 
  
  integ : int (default : 0)
      Specifies how many time to difference the dependent variables.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
  
  use_ols_covariance : Boolean
      If true use OLS covariance; if false use estimated covariance
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","VECTOR","AUTOREGRESSION","(","VAR",")","MODELS","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","time","series","data","that","will","be","used",".","lags",":","int","Field","to","specify","how","many","lag","terms","the","model","will","have",".","integ",":","int","(","default",":","0",")","Specifies","how","many","time","to","difference","the","dependent","variables",".","target",":","str","(","pd.DataFrame",")","or","int","(","np.array",")","Specifies","which","column","name","or","array","index","to","use",".","By","default",",","first","column\/array","will","be","selected","as","the","dependent","variable",".","use_ols_covariance",":","Boolean","If","true","use","OLS","covariance",";","if","false","use","estimated","covariance"]
docstr: Makes forecast with the estimated model
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps ahead would you like to forecast?
  
  Returns
  ----------
  - pd.DataFrame with predicted values
docstr_tok: ["Makes","forecast","with","the","estimated","model","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","ahead","would","you","like","to","forecast","?","Returns","--","--","--","--","--","-","pd.DataFrame","with","predicted","values"]
code: def predict(self, h=5):
      """Makes forecast with the estimated model
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps ahead would you like to forecast?
  
      Returns
      ----------
      - pd.DataFrame with predicted values
      """
  
      if self.latent_variables.estimated is False:
          raise Exception("No latent variables estimated!")
      else:
          # Retrieve data, dates and (transformed) latent variables
          mu, Y = self._model(self.latent_variables.get_z_values())
          date_index = self.shift_dates(h)
          t_params = self.transform_z()
  
          # Expectation
          exps = self._forecast_mean(h, t_params, Y, None, None)
  
          for variable in range(0, exps.shape[0]):
              forecasted_values = exps[variable][-h:]
              if variable == 0:
                  result = pd.DataFrame(forecasted_values)
                  result.rename(
                      columns={0: self.data_name[variable]},
                      inplace=True,
                  )
                  result.index = date_index[-h:]
              else:
                  result[self.data_name[variable]] = forecasted_values
  
          return result
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import datetime
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .var_recursions import create_design_matrix, custom_covariance_matrix, var_likelihood
label_desc: Use image node_id 17 for calling the VAR obj's underlying member method code with example usage: obj.predict(h) and returns: result