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: Plots forecasts with the estimated model against data
      (Simulated prediction with data)
  
  Parameters
  ----------
  h : int (default : 5)
      How many steps to forecast
  
  Returns
  ----------
  - Plot of the forecast against data 
docstr_tok: ["Plots","forecasts","with","the","estimated","model","against","data","(","Simulated","prediction","with","data",")","Parameters","--","--","--","--","--","h",":","int","(","default",":","5",")","How","many","steps","to","forecast","Returns","--","--","--","--","--","-","Plot","of","the","forecast","against","data"]
code: def plot_predict_is(self, h=5, **kwargs):
      """Plots forecasts with the estimated model against data
          (Simulated prediction with data)
  
      Parameters
      ----------
      h : int (default : 5)
          How many steps to forecast
  
      Returns
      ----------
      - Plot of the forecast against data
      """
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      figsize = kwargs.get("figsize", (10, 7))
  
      date_index = self.index[-h:]
      predictions = self.predict_is(h)
  
      for variable in range(self.ylen):
          plt.figure(figsize=figsize)
          data = self.data[variable][-h:]
          plt.plot(
              date_index, data, label=self.data_name[variable] + " Data"
          )
          plt.plot(
              date_index,
              predictions.ix[:, variable].values,
              label="Predictions",
              c="black",
          )
          plt.title(self.data_name[variable])
          plt.legend(loc=2)
          plt.show()
  
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 18 for calling the VAR obj's underlying member method code with example usage: obj.plot_predict_is(h) without return types