old_contents: 
  import numpy as np
  import matplotlib.pyplot as plt
  from scipy.stats import norm
  from quantecon import LinearStateSpace
  
  phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
  sigma = 0.1
  
  A = [[phi_1, phi_2, phi_3, phi_4],
       [1,     0,     0,     0],
       [0,     1,     0,     0],
       [0,     0,     1,     0]]
  C = [sigma, 0, 0, 0]
  G = [1, 0, 0, 0]
  
  T = 30
  ar = LinearStateSpace(A, C, G)
  
  ymin, ymax = -0.8, 1.25
  
  fig, ax = plt.subplots(figsize=(8, 4))
  
  ax.set_xlim(ymin, ymax)
  ax.set_xlabel(r'$y_t$', fontsize=16)
  
  x, y = ar.replicate(T=T, num_reps=100000)
  mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()
  f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y)))
  
  y = y.flatten()
  ax.hist(y, bins=50, normed=True, alpha=0.4)
  
  ygrid = np.linspace(ymin, ymax, 150)
  ax.plot(ygrid, f_y.pdf(ygrid), 'k-', lw=2, alpha=0.8, label='true density')
  ax.legend()
  plt.show()
  
new_contents: 
  import numpy as np
  import matplotlib.pyplot as plt
  from scipy.stats import norm
  from quantecon import LinearStateSpace
  
  phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
  sigma = 0.1
  
  A = [[phi_1, phi_2, phi_3, phi_4],
       [1,     0,     0,     0],
       [0,     1,     0,     0],
       [0,     0,     1,     0]]
  C = [[sigma], [0], [0], [0]]
  G = [1, 0, 0, 0]
  
  T = 30
  ar = LinearStateSpace(A, C, G)
  
  ymin, ymax = -0.8, 1.25
  
  fig, ax = plt.subplots(figsize=(8, 4))
  
  ax.set_xlim(ymin, ymax)
  ax.set_xlabel(r'$y_t$', fontsize=16)
  
  x, y = ar.replicate(T=T, num_reps=100000)
  mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()
  f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y)))
  
  y = y.flatten()
  ax.hist(y, bins=50, normed=True, alpha=0.4)
  
  ygrid = np.linspace(ymin, ymax, 150)
  ax.plot(ygrid, f_y.pdf(ygrid), 'k-', lw=2, alpha=0.8, label='true density')
  ax.legend()
  plt.show()
  
subject: Fix C from row to column vector