old_contents: """
  Autoregressive Moving Average (ARMA) Model
  """
  import numpy as np
  import statsmodels.api as sm
  
  # Generate some data from an ARMA process
  from statsmodels.tsa.arima_process import arma_generate_sample
  
  np.random.seed(12345)
  arparams = np.array([.75, -.25])
  maparams = np.array([.65, .35])
  
  # The conventions of the arma_generate function require that we specify a
  # 1 for the zero-lag of the AR and MA parameters and that the AR parameters
  # be negated.
  arparams = np.r_[1, -arparams]
  maparam = np.r_[1, maparams]
  nobs = 250
  y = arma_generate_sample(arparams, maparams, nobs)
  
  # Now, optionally, we can add some dates information. For this example,
  # we'll use a pandas time series.
  import pandas
  dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
  y = pandas.TimeSeries(y, index=dates)
  arma_mod = sm.tsa.ARMA(y, order=(2, 2))
  arma_res = arma_mod.fit(trend='nc', disp=-1)
  
new_contents: """
  Autoregressive Moving Average (ARMA) Model
  """
  import numpy as np
  import statsmodels.api as sm
  
  # Generate some data from an ARMA process
  from statsmodels.tsa.arima_process import arma_generate_sample
  
  np.random.seed(12345)
  arparams = np.array([.75, -.25])
  maparams = np.array([.65, .35])
  
  # The conventions of the arma_generate function require that we specify a
  # 1 for the zero-lag of the AR and MA parameters and that the AR parameters
  # be negated.
  ar = np.r_[1, -arparams]
  ma = np.r_[1, maparams]
  nobs = 250
  y = arma_generate_sample(ar, ma, nobs)
  
  # Now, optionally, we can add some dates information. For this example,
  # we'll use a pandas time series.
  import pandas
  dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
  y = pandas.TimeSeries(y, index=dates)
  arma_mod = sm.tsa.ARMA(y, order=(2, 2))
  arma_res = arma_mod.fit(trend='nc', disp=-1)
  
subject: Update ex_arma2.py