title: Pandas Rolling Regression: y_fitted vs y_predict difference
body: <p>First time posting, please excuse any mistakes I made. </p>
  
  <p>I just ran a rolling regression with the model: Y ~ X1 + X2 + X3 + X3 in pandas.
  I know that model.y_fitted returns the fitted values, which is just X * beta.</p>
  
  <p>But what is y_predict? It returned slightly different values compared to y_fitted. I am very curious what type of calculation it is doing. </p>
  
  <p>Sample Code:</p>
  
  <pre><code>model = pd.ols(y=Y, x=X, intercept=False, window=500)
  model.y_fitted
  model.y_predict
  </code></pre>
  
  <p>The above returns slightly different values.
  <a href="http://i.stack.imgur.com/PcqJa.png" rel="nofollow">Click here screenshot of Dataframe</a></p>
  
  <p>Thanks for your help.</p>