class_docstr: Estimate VAR(p) process with fixed number of lags
  
  Parameters
  ----------
  endog : ndarray
  endog_lagged : ndarray
  params : ndarray
  sigma_u : ndarray
  lag_order : int
  model : VAR model instance
  trend : str {'n', 'c', 'ct'}
  names : array_like
      List of names of the endogenous variables in order of appearance in `endog`.
  dates
  
  Attributes
  ----------
  aic
  bic
  bse
  coefs : ndarray (p x K x K)
      Estimated A_i matrices, A_i = coefs[i-1]
  cov_params
  dates
  detomega
  df_model : int
  df_resid : int
  endog
  endog_lagged
  fittedvalues
  fpe
  intercept
  info_criteria
  k_ar : int
  k_trend : int
  llf
  model
  names
  neqs : int
      Number of variables (equations)
  nobs : int
  n_totobs : int
  params
  k_ar : int
      Order of VAR process
  params : ndarray (Kp + 1) x K
      A_i matrices and intercept in stacked form [int A_1 ... A_p]
  pvalue
  names : list
      variables names
  resid
  sigma_u : ndarray (K x K)
      Estimate of white noise process variance Var[u_t]
  sigma_u_mle
  stderr
  trenorder
  tvalues
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code: def __init__(
      self,
      endog,
      endog_lagged,
      params,
      sigma_u,
      lag_order,
      A=None,
      B=None,
      A_mask=None,
      B_mask=None,
      model=None,
      trend="c",
      names=None,
      dates=None,
  ):
      self.model = model
      self.endog = endog
      self.endog_lagged = endog_lagged
      self.dates = dates
  
      self.n_totobs, self.neqs = self.endog.shape
      self.nobs = self.n_totobs - lag_order
      k_trend = util.get_trendorder(trend)
      if k_trend > 0:  # make this the polynomial trend order
          trendorder = k_trend - 1
      else:
          trendorder = None
      self.k_trend = k_trend
      self.k_exog = k_trend  # now (0.9) required by VARProcess
      self.trendorder = trendorder
  
      self.exog_names = util.make_lag_names(names, lag_order, k_trend)
      self.params = params
      self.sigma_u = sigma_u
  
      # Each matrix needs to be transposed
      reshaped = self.params[self.k_trend :]
      reshaped = reshaped.reshape((lag_order, self.neqs, self.neqs))
  
      # Need to transpose each coefficient matrix
      intercept = self.params[0]
      coefs = reshaped.swapaxes(1, 2).copy()
  
      # SVAR components
      # TODO: if you define these here, you do not also have to define
      # them in SVAR process, but I left them for now -ss
      self.A = A
      self.B = B
      self.A_mask = A_mask
      self.B_mask = B_mask
  
      super().__init__(coefs, intercept, sigma_u, A, B, names=names)
  
imports: import numpy
  import numpy.linalg
  from numpy.linalg import slogdet
  from statsmodels.tools.decorators import deprecated_alias
  from statsmodels.tools.numdiff import approx_fprime, approx_hess
  import statsmodels.tsa.base.tsa_model
  from statsmodels.tsa.vector_ar.irf import IRAnalysis
  import statsmodels.tsa.vector_ar.util
  from statsmodels.tsa.vector_ar.var_model import VARProcess, VARResults
label_desc: Use image node_id 1 to create a new SVARResults object from inherited base classes: SVARProcess, VARResults with example: obj = SVARResults(endog, endog_lagged, params, sigma_u, lag_order, A, B, A_mask, B_mask, model, trend, names, dates)