def test_cochrane_orcutt_stock_data(self):
"""
ref : http://www.ats.ucla.edu/stat/stata/examples/chp/chpstata8.htm
data : http://www.ats.ucla.edu/stat/examples/chp/p203.txt
"""
expenditure = [
214.6, 217.7, 219.6, 227.2, 230.9, 233.3, 234.1, 232.3, 233.7, 236.5,
238.7, 243.2, 249.4, 254.3, 260.9, 263.3, 265.6, 268.2, 270.4, 275.6
]
stock = [
159.3, 161.2, 162.8, 164.6, 165.9, 167.9, 168.3, 169.7, 170.5, 171.6,
173.9, 176.1, 178.0, 179.1, 180.2, 181.2, 181.6, 182.5, 183.3, 184.3
]
Y = np.array(expenditure)
regressors = np.array(stock).reshape(len(stock), 1)
regARIMA = RegressionARIMA.fit_cochrane_orcutt(Y, regressors, 11, sc=self.sc)
beta = regARIMA.regressionCoeff
rho = regARIMA.arimaCoeff[0]
self.assertAlmostEqual(rho, 0.8241, delta=0.001)
self.assertAlmostEqual(beta[0], -235.4889, delta=0.1)
self.assertAlmostEqual(beta[1], 2.75306, delta=0.001)
def test_cochrane_orcutt_stock_data(self):
"""
ref : http://www.ats.ucla.edu/stat/stata/examples/chp/chpstata8.htm
data : http://www.ats.ucla.edu/stat/examples/chp/p203.txt
"""
expenditure = [
214.6, 217.7, 219.6, 227.2, 230.9, 233.3, 234.1, 232.3, 233.7,
236.5, 238.7, 243.2, 249.4, 254.3, 260.9, 263.3, 265.6, 268.2,
270.4, 275.6
]
stock = [
159.3, 161.2, 162.8, 164.6, 165.9, 167.9, 168.3, 169.7, 170.5,
171.6, 173.9, 176.1, 178.0, 179.1, 180.2, 181.2, 181.6, 182.5,
183.3, 184.3
]
Y = np.array(expenditure)
regressors = np.array(stock).reshape(len(stock), 1)
regARIMA = RegressionARIMA.fit_cochrane_orcutt(Y,
regressors,
11,
sc=self.sc)
beta = regARIMA.regressionCoeff
rho = regARIMA.arimaCoeff[0]
self.assertAlmostEqual(rho, 0.8241, delta=0.001)
self.assertAlmostEqual(beta[0], -235.4889, delta=0.1)
self.assertAlmostEqual(beta[1], 2.75306, delta=0.001)
