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: Creates covariance matrix for the estimators
  
  Parameters
  ----------
  method : str
      Estimation method
  
  Returns
  ----------
  A Covariance Matrix
docstr_tok: ["Creates","covariance","matrix","for","the","estimators","Parameters","--","--","--","--","--","method",":","str","Estimation","method","Returns","--","--","--","--","--","A","Covariance","Matrix"]
code: def estimator_cov(self, method):
      """Creates covariance matrix for the estimators
  
      Parameters
      ----------
      method : str
          Estimation method
  
      Returns
      ----------
      A Covariance Matrix
      """
  
      Y = np.array([reg[self.lags :] for reg in self.data])
      Z = self._create_Z(Y)
      if method == "OLS":
          sigma = self.ols_covariance()
      else:
          sigma = self.custom_covariance(
              self.latent_variables.get_z_values()
          )
      return np.kron(np.linalg.inv(np.dot(Z, np.transpose(Z))), sigma)
  
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 11 for calling the VAR obj's underlying member method code with example usage: obj.estimator_cov(method) and returns: np