class_docstr: generic moment tests or conditional moment tests for Quasi-MLE
  
  This is a generic class based on Tauchen 1985
  
  The main method is `chisquare` which returns the result of the
  conditional moment test.
  
  Warning: name of class and of methods will likely be changed
  
  Parameters
  ----------
  score : ndarray, 1-D
      moment condition used in estimation, score of log-likelihood function
  score_deriv : ndarray
      derivative of score function with respect to the parameters that are
      estimated. This is the Hessian in quasi-maximum likelihood
  moments : ndarray, 1-D
      moments that are tested to be zero. They do not need to be derived
      from a likelihood function.
  moments_deriv : ndarray
      derivative of the moment function with respect to the parameters that
      are estimated
  cov_moments : ndarray
      An estimate for the joint (expected) covariance of score and test
      moments. This can be a heteroscedasticity or correlation robust
      covariance estimate, i.e. the inner part of a sandwich covariance.
docstr: statistic, p-value and degrees of freedom of separate moment test
  
  currently two sided test only
  
  TODO: This can use generic ztest/ttest features and return
  ContrastResults
docstr_tok: ["statistic",",","p-value","and","degrees","of","freedom","of","separate","moment","test","currently","two","sided","test","only","TODO",":","This","can","use","generic","ztest\/ttest","features","and","return","ContrastResults"]
code: def ztest(self):
      """statistic, p-value and degrees of freedom of separate moment test
  
      currently two sided test only
  
      TODO: This can use generic ztest/ttest features and return
      ContrastResults
      """
      diff = self.moments_constraint
      bse = np.sqrt(np.diag(self.cov_mom_constraints))
  
      # Newey uses a generalized inverse
      stat = diff / bse
      pval = stats.norm.sf(np.abs(stat)) * 2
      return stat, pval
  
imports: import warnings
  import numpy
  from scipy import stats
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.regression.linear_model import OLS
label_desc: Use image node_id 5 for calling the CMTTauchen obj's underlying member method code with example usage: obj.ztest() and returns: stat,  pval