title: Python statsmodel.tsa.MarkovAutoregression using current real GNP/GDP data
body: <p>I have been using statsmodel.tsa.MarkovAutoregressio to replicate Hamilton's markov switching model published in 1989. If using the Hamilton data (real GNP in 1982 dollar) I could have the same result as the code example / the paper showed. However, when I used current available real GNP or GDP data (in 2009 dollar) and took their log difference (quarterly) as input, the model doesn't give satisfactory results. </p>
  
  <p>I plotted the log difference of Hamilton gnp and that's from the current available real GNP. They are quite close with slight differences.</p>
  
  <p>Can anyone enlighten me why it is the case? Does it have anything to do with the seasonality adjustment of current GNP data? If so, is there is any way to counter it?</p>
  
  <p>Result using current available GNP<br>
  <a href="https://i.stack.imgur.com/uJifz.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/uJifz.png" alt="Result using current available GNP"></a></p>
  
  <p>Result using paper provided GNP
  <a href="https://i.stack.imgur.com/0Yra6.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/0Yra6.png" alt="Result using paper provided GNP"></a></p>