title: Non-Linearity Test NaN error
body: <p>I wanted to try statsmodel's linear_harvey_collier test with an easy example. However, I get nan as a result. Can you see, where my error lies? </p>
  
  <pre class="lang-py prettyprint-override"><code>import numpy as np
  from statsmodels.regression.linear_model import OLS
  
  np.random.seed(44)
  n_samples, n_features = 50, 4
  X = np.random.randn(n_samples, n_features)
  coef=np.random.uniform(-12,12,4)
  y = np.dot(X, coef)
  var = 400
  y += var**(1/2) * np.random.normal(size=n_samples)
  regr=OLS(y, X).fit()
  print(regr.params)
  print(regr.summary())
  
  sms.linear_harvey_collier(regr)
  </code></pre>
  
  <p>I get the result Ttest_1sampResult(statistic=nan, pvalue=nan).</p>
  
  <p>If I perform the test while exluding one variable I get a result: </p>
  
  <pre class="lang-py prettyprint-override"><code>X3=X[:,:3]
  regr3=OLS(y, X3).fit()
  In [1]: sms.linear_harvey_collier(regr3)
  Out[2]: Ttest_1sampResult(statistic=0.2447803429683807, pvalue=0.806727747845282)
  </code></pre>
  
  <p>Is there a problem with not adding a constant and intercept? This is just a feeling and if there is indeed a problem, I don't understand why.</p>