#!/usr/bin/env python
# coding: utf-8

# In[58]:


get_ipython().run_line_magic('matplotlib', 'inline')

import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.statespace import mlemodel
from statsmodels.tsa.statespace.tests import test_representation as t
import matplotlib.pyplot as plt

np.set_printoptions(precision=4, suppress=True, linewidth=120)


# In[95]:


# Get the multiple endog test case
# Note: it directly uses a KalmanFilter instance...
test = t.Clark1989()
test.run_filter()

# ...so wrap it into an MLEModel instance
# Note: zero parameters here
x = mlemodel.MLEModel(endog=test.model.endog.T, k_states=test.model.k_states)
x.ssm = test.model
res = x.filter([])

# Summary: note that the diagnostics table shows two
# numbers for each test corresponding to the two endog
print res.summary()

# Diagnostics for the first endog
fig1 = res.plot_diagnostics(0, figsize=(10,6))
fig1.suptitle('Diagnostics for y1', fontsize='xx-large')
fig1.subplots_adjust(top=0.87)

# Diagnostics for the second endog
fig2 = res.plot_diagnostics(1, figsize=(10,6))
fig2.suptitle('Diagnostics for y2', fontsize='xx-large')
fig2.subplots_adjust(top=0.87);