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)
def test_cochrane_orcutt_empdata_with_maxiter(self):
metal = [
44.2, 44.3, 44.4, 43.4, 42.8, 44.3, 44.4, 44.8, 44.4, 43.1, 42.6,
42.4, 42.2, 41.8, 40.1, 42, 42.4, 43.1, 42.4, 43.1, 43.2, 42.8, 43,
42.8, 42.5, 42.6, 42.3, 42.9, 43.6, 44.7, 44.5, 45, 44.8, 44.9,
45.2, 45.2, 45, 45.5, 46.2, 46.8, 47.5, 48.3, 48.3, 49.1, 48.9,
49.4, 50, 50, 49.6, 49.9, 49.6, 50.7, 50.7, 50.9, 50.5, 51.2, 50.7,
50.3, 49.2, 48.1
]
vendor = [
322.0, 317, 319, 323, 327, 328, 325, 326, 330, 334, 337, 341, 322,
318, 320, 326, 332, 334, 335, 336, 335, 338, 342, 348, 330, 326,
329, 337, 345, 350, 351, 354, 355, 357, 362, 368, 348, 345, 349,
355, 362, 367, 366, 370, 371, 375, 380, 385, 361, 354, 357, 367,
376, 381, 381, 383, 384, 387, 392, 396
]
Y = np.array(metal)
regressors = np.array(vendor).reshape(len(vendor), 1)
regARIMA = RegressionARIMA.fit_model(Y,
regressors,
method="cochrane-orcutt",
optimizationArgs=[1],
sc=self.sc)
beta = regARIMA.regressionCoeff
self.assertAlmostEqual(beta[0], 28.918, delta=0.01)
self.assertAlmostEqual(beta[1], 0.0479, delta=0.001)
def test_cochrane_orcutt_empdata_with_maxiter(self):
metal = [
44.2, 44.3, 44.4, 43.4, 42.8, 44.3, 44.4, 44.8, 44.4, 43.1, 42.6, 42.4, 42.2,
41.8, 40.1, 42, 42.4, 43.1, 42.4, 43.1, 43.2, 42.8, 43, 42.8, 42.5, 42.6, 42.3, 42.9, 43.6,
44.7, 44.5, 45, 44.8, 44.9, 45.2, 45.2, 45, 45.5, 46.2, 46.8, 47.5, 48.3, 48.3, 49.1, 48.9,
49.4, 50, 50, 49.6, 49.9, 49.6, 50.7, 50.7, 50.9, 50.5, 51.2, 50.7, 50.3, 49.2, 48.1
]
vendor = [
322.0, 317, 319, 323, 327, 328, 325, 326, 330, 334, 337, 341, 322, 318, 320,
326, 332, 334, 335, 336, 335, 338, 342, 348, 330, 326, 329, 337, 345, 350, 351, 354, 355, 357,
362, 368, 348, 345, 349, 355, 362, 367, 366, 370, 371, 375, 380, 385, 361, 354, 357, 367, 376,
381, 381, 383, 384, 387, 392, 396
]
Y = np.array(metal)
regressors = np.array(vendor).reshape(len(vendor), 1)
regARIMA = RegressionARIMA.fit_model(Y, regressors, method="cochrane-orcutt", optimizationArgs=[1], sc=self.sc)
beta = regARIMA.regressionCoeff
self.assertAlmostEqual(beta[0], 28.918, delta=0.01)
self.assertAlmostEqual(beta[1], 0.0479, delta=0.001)
