title: Python and Statsmodels: How to evaluate the F-test of joint parameters in ARMA model?
body: <p>I have an ARMA model like this (it is actually a AR(4) model):</p>
  
  <pre><code>arRes4=sm.tsa.ARMA(res2AR, (4,0),freq='Q').fit() 
  </code></pre>
  
  <p>with 5 parameters. The code above is working well.</p>
  
  <p>I have tried several possibilities to evaluate the f_test and it is not clear for me it is not working:</p>
  
  <pre><code>hypothesis = '(ar.L1.y = 0),(ar.L2.y = 0),(ar.L3.y = 0),(ar.L4.y = 0)'
  
  fTest=arRes4.f_test(hypothesis)
  </code></pre>
  
  <p>It raises the error:</p>
  
  <pre><code>    raise ValueError('need covariance of parameters for computing '
  ValueError: need covariance of parameters for computing F statistics
  </code></pre>
  
  <p>It is not clear why it asks for the covariance matrix. However, I tried</p>
  
  <p>fTest=arRes4.f_test(hypothesis,arRes4.cov_params().as_matrix())</p>
  
  <p>It raises the error</p>
  
  <pre><code>    cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p)
  TypeError: cov_params() got an unexpected keyword argument 'r_matrix'
  </code></pre>
  
  <p>I also tried replace "hypothesis" above by</p>
  
  <pre><code>R=np.array([[0,1,0,0,0],[0,0,1,0,0],[0,0,1,0,0],[0,0,0,1,0],[0,0,0,0,1]])
  </code></pre>
  
  <p>The same errors happen!</p>