本文整理汇总了Python中statsmodels.tsa.statespace.mlemodel.MLEModel.ssm["design",0,0]方法的典型用法代码示例。如果您正苦于以下问题：Python MLEModel.ssm["design",0,0]方法的具体用法？Python MLEModel.ssm["design",0,0]怎么用？Python MLEModel.ssm["design",0,0]使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类statsmodels.tsa.statespace.mlemodel.MLEModel的用法示例。
在下文中一共展示了MLEModel.ssm["design",0,0]方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_diagnostics_nile_eviews
# 需要导入模块: from statsmodels.tsa.statespace.mlemodel import MLEModel [as 别名]
# 或者: from statsmodels.tsa.statespace.mlemodel.MLEModel import ssm["design",0,0] [as 别名]
def test_diagnostics_nile_eviews():
# Test the diagnostic tests using the Nile dataset. Results are from
# "Fitting State Space Models with EViews" (Van den Bossche 2011,
# Journal of Statistical Software).
# For parameter values, see Figure 2
# For Ljung-Box and Jarque-Bera statistics and p-values, see Figure 5
# The Heteroskedasticity statistic is not provided in this paper.
niledata = nile.data.load_pandas().data
niledata.index = pd.date_range("1871-01-01", "1970-01-01", freq="AS")
mod = MLEModel(
niledata["volume"],
k_states=1,
initialization="approximate_diffuse",
initial_variance=1e15,
loglikelihood_burn=1,
)
mod.ssm["design", 0, 0] = 1
mod.ssm["obs_cov", 0, 0] = np.exp(9.600350)
mod.ssm["transition", 0, 0] = 1
mod.ssm["selection", 0, 0] = 1
mod.ssm["state_cov", 0, 0] = np.exp(7.348705)
res = mod.filter([])
# Test Ljung-Box
# Note: only 3 digits provided in the reference paper
actual = res.test_serial_correlation(method="ljungbox", lags=10)[0, :, -1]
assert_allclose(actual, [13.117, 0.217], atol=1e-3)
# Test Jarque-Bera
actual = res.test_normality(method="jarquebera")[0, :2]
assert_allclose(actual, [0.041686, 0.979373], atol=1e-5)示例2: test_diagnostics_nile_durbinkoopman
# 需要导入模块: from statsmodels.tsa.statespace.mlemodel import MLEModel [as 别名]
# 或者: from statsmodels.tsa.statespace.mlemodel.MLEModel import ssm["design",0,0] [as 别名]
def test_diagnostics_nile_durbinkoopman():
# Test the diagnostic tests using the Nile dataset. Results are from
# Durbin and Koopman (2012); parameter values reported on page 37; test
# statistics on page 40
niledata = nile.data.load_pandas().data
niledata.index = pd.date_range("1871-01-01", "1970-01-01", freq="AS")
mod = MLEModel(
niledata["volume"],
k_states=1,
initialization="approximate_diffuse",
initial_variance=1e15,
loglikelihood_burn=1,
)
mod.ssm["design", 0, 0] = 1
mod.ssm["obs_cov", 0, 0] = 15099.0
mod.ssm["transition", 0, 0] = 1
mod.ssm["selection", 0, 0] = 1
mod.ssm["state_cov", 0, 0] = 1469.1
res = mod.filter([])
# Test Ljung-Box
# Note: only 3 digits provided in the reference paper
actual = res.test_serial_correlation(method="ljungbox", lags=9)[0, 0, -1]
assert_allclose(actual, [8.84], atol=1e-2)
# Test Jarque-Bera
# Note: The book reports 0.09 for Kurtosis, because it is reporting the
# statistic less the mean of the Kurtosis distribution (which is 3).
norm = res.test_normality(method="jarquebera")[0]
actual = [norm[0], norm[2], norm[3]]
assert_allclose(actual, [0.05, -0.03, 3.09], atol=1e-2)
# Test Heteroskedasticity
# Note: only 2 digits provided in the book
actual = res.test_heteroskedasticity(method="breakvar")[0, 0]
assert_allclose(actual, [0.61], atol=1e-2)