title: numpy and statsmodels give different values when calculating correlations, How to interpret this?
body: <p>I can't find a reason why calculating the correlation between two series A and B using <code>numpy.correlate</code> gives me different results than the ones I obtain using <code>statsmodels.tsa.stattools.ccf</code></p>
  
  <p>Here's an example of this difference I mention:</p>
  
  <pre><code>import numpy as np
  from matplotlib import pyplot as plt
  from statsmodels.tsa.stattools import ccf
  
  #Calculate correlation using numpy.correlate
  def corr(x,y):
      result = numpy.correlate(x, y, mode='full')
      return result[result.size/2:]
  
  #This are the data series I want to analyze
  A = np.array([np.absolute(x) for x in np.arange(-1,1.1,0.1)])
  B = np.array([x for x in np.arange(-1,1.1,0.1)])
  
  #Using numpy i get this
  plt.plot(corr(B,A))
  </code></pre>
  
  <p><img src="https://i.stack.imgur.com/FWThP.png" alt="enter image description here">   </p>
  
  <pre><code>#Using statsmodels i get this
  plt.plot(ccf(B,A,unbiased=False))
  </code></pre>
  
  <p><img src="https://i.stack.imgur.com/iuEkr.png" alt="enter image description here"></p>
  
  <p>The results seem qualitatively different, where does this difference come from?</p>