def test_init_endog():
index = pd.date_range(start='1950-01-01', periods=10, freq='D')
endog = [
np.ones(10),
pd.Series(np.ones(10), index=index),
np.ones((10, 1)),
pd.DataFrame(np.ones((10, 1)), index=index)
]
for _endog in endog:
mod = markov_switching.MarkovSwitching(_endog, k_regimes=2)
assert_equal(mod.nobs, 10)
assert_equal(mod.endog, _endog.squeeze())
assert_equal(mod.k_regimes, 2)
assert_equal(mod.tvtp, False)
assert_equal(mod.k_tvtp, 0)
assert_equal(mod.k_params, 2)
# Invalid: k_regimes < 2
endog = np.ones(10)
assert_raises(ValueError,
markov_switching.MarkovSwitching,
endog,
k_regimes=1)
# Invalid: multiple endog columns
endog = np.ones((10, 2))
assert_raises(ValueError,
markov_switching.MarkovSwitching,
endog,
k_regimes=2)
def test_init_exog_tvtp():
endog = np.ones(10)
exog_tvtp = np.c_[np.ones((10, 1)), (np.arange(10) + 1)[:, np.newaxis]]
mod = markov_switching.MarkovSwitching(endog, k_regimes=2,
exog_tvtp=exog_tvtp)
assert_equal(mod.tvtp, True)
assert_equal(mod.k_tvtp, 2)
# Invalid exog_tvtp (too many obs)
exog_tvtp = np.c_[np.ones((11, 1)), (np.arange(11) + 1)[:, np.newaxis]]
assert_raises(ValueError, markov_switching.MarkovSwitching, endog,
k_regimes=2, exog_tvtp=exog_tvtp)
def test_initial_probabilities():
endog = np.ones(10)
mod = markov_switching.MarkovSwitching(endog, k_regimes=2)
params = np.r_[0.5, 0.5, 1.]
# Valid known initial probabilities
mod.initialize_known([0.2, 0.8])
assert_allclose(mod.initial_probabilities(params), [0.2, 0.8])
# Invalid known initial probabilities (too many elements)
assert_raises(ValueError, mod.initialize_known, [0.2, 0.2, 0.6])
# Invalid known initial probabilities (doesn't sum to 1)
assert_raises(ValueError, mod.initialize_known, [0.2, 0.2])
# Valid steady-state probabilities
mod.initialize_steady_state()
assert_allclose(mod.initial_probabilities(params), [0.5, 0.5])
# Invalid steady-state probabilities (when mod has tvtp)
endog = np.ones(10)
mod = markov_switching.MarkovSwitching(endog, k_regimes=2, exog_tvtp=endog)
assert_raises(ValueError, mod.initialize_steady_state)
def test_transition_matrix():
# k_regimes = 2
endog = np.ones(10)
mod = markov_switching.MarkovSwitching(endog, k_regimes=2)
params = np.r_[0., 0., 1.]
transition_matrix = np.zeros((2, 2, 1))
transition_matrix[1, :] = 1.
assert_allclose(mod.regime_transition_matrix(params), transition_matrix)
# k_regimes = 3
endog = np.ones(10)
mod = markov_switching.MarkovSwitching(endog, k_regimes=3)
params = np.r_[[0] * 3, [0.2] * 3, 1.]
transition_matrix = np.zeros((3, 3, 1))
transition_matrix[1, :, 0] = 0.2
transition_matrix[2, :, 0] = 0.8
assert_allclose(mod.regime_transition_matrix(params), transition_matrix)
# k_regimes = 2, tvtp
endog = np.ones(10)
exog_tvtp = np.c_[np.ones((10, 1)), (np.arange(10) + 1)[:, np.newaxis]]
mod = markov_switching.MarkovSwitching(endog,
k_regimes=2,
exog_tvtp=exog_tvtp)
# If all TVTP regression coefficients are zero, then the logit transform
# results in exp(0) / (1 + exp(0)) = 0.5 for all parameters; since it's
# k_regimes=2 the remainder calculation is also 0.5.
params = np.r_[0, 0, 0, 0]
assert_allclose(mod.regime_transition_matrix(params), 0.5)
# Manually compute the TVTP coefficients
params = np.r_[1, 2, 1, 2]
transition_matrix = np.zeros((2, 2, 10))
coeffs0 = np.sum(exog_tvtp, axis=1)
p11 = np.exp(coeffs0) / (1 + np.exp(coeffs0))
transition_matrix[0, 0, :] = p11
transition_matrix[1, 0, :] = 1 - p11
coeffs1 = np.sum(2 * exog_tvtp, axis=1)
p21 = np.exp(coeffs1) / (1 + np.exp(coeffs1))
transition_matrix[0, 1, :] = p21
transition_matrix[1, 1, :] = 1 - p21
assert_allclose(mod.regime_transition_matrix(params),
transition_matrix,
atol=1e-10)
# k_regimes = 3, tvtp
endog = np.ones(10)
exog_tvtp = np.c_[np.ones((10, 1)), (np.arange(10) + 1)[:, np.newaxis]]
mod = markov_switching.MarkovSwitching(endog,
k_regimes=3,
exog_tvtp=exog_tvtp)
# If all TVTP regression coefficients are zero, then the logit transform
# results in exp(0) / (1 + exp(0) + exp(0)) = 1/3 for all parameters;
# since it's k_regimes=3 the remainder calculation is also 1/3.
params = np.r_[[0] * 12]
assert_allclose(mod.regime_transition_matrix(params), 1 / 3)
# Manually compute the TVTP coefficients for the first column
params = np.r_[[0] * 6, [2] * 6]
transition_matrix = np.zeros((3, 3, 10))
p11 = np.zeros(10)
p12 = 2 * np.sum(exog_tvtp, axis=1)
tmp = np.exp(np.c_[p11, p12]).T
transition_matrix[:2, 0, :] = tmp / (1 + np.sum(tmp, axis=0))
transition_matrix[2,
0, :] = (1 - np.sum(transition_matrix[:2, 0, :], axis=0))
assert_allclose(mod.regime_transition_matrix(params)[:, 0, :],
transition_matrix[:, 0, :],
atol=1e-10)
