new_contents: from statsmodels.datasets.macrodata import load_pandas
  from statsmodels.tsa.base.datetools import dates_from_range
  from statsmodels.tsa.arima_model import ARIMA
  import matplotlib.pyplot as plt
  import numpy as np
  import statsmodels.api as sm
  plt.interactive(False)
  
  # let's examine an ARIMA model of CPI
  
  cpi = load_pandas().data["cpi"]
  dates = dates_from_range("1959q1", "2009q3")
  cpi.index = dates
  
  res = ARIMA(cpi, (1,1,1), freq='Q').fit()
  print res.summary()
  
  # we can look at the series
  cpi.diff().plot()
  
  # maybe logs are better
  log_cpi = np.log(cpi)
  
  # check the ACF and PCF plots
  acf, confint_acf = sm.tsa.acf(log_cpi.diff().values[1:], confint=95)
  # center the confidence intervals about zero
  #confint_acf -= confint_acf.mean(1)[:,None]
  pacf = sm.tsa.pacf(log_cpi.diff().values[1:], method='ols')
  # confidence interval is now an option to pacf
  from scipy import stats
  confint_pacf = stats.norm.ppf(1-.025) * np.sqrt(1/202.)
  
  fig = plt.figure()
  ax = fig.add_subplot(121)
  ax.set_title("Autocorrelation")
  ax.plot(range(41), acf, 'bo', markersize=5)
  ax.vlines(range(41), 0, acf)
  ax.fill_between(range(41), confint_acf[:,0], confint_acf[:,1], alpha=.25)
  fig.tight_layout()
  ax = fig.add_subplot(122, sharey=ax)
  ax.vlines(range(41), 0, pacf)
  ax.plot(range(41), pacf, 'bo', markersize=5)
  ax.fill_between(range(41), -confint_pacf, confint_pacf, alpha=.25)
  
  
  #NOTE: you'll be able to just to this when tsa-plots is in master
  #sm.graphics.acf_plot(x, nlags=40)
  #sm.graphics.pacf_plot(x, nlags=40)
  
  
  # still some seasonality
  # try an arma(1,1) with ma(4) term
  
subject: Add first pass at ARIMA example. Work in Progress.