from __future__ import print_function import numpy as np #import statsmodels.linear_model.regression as smreg from scipy import special import statsmodels.api as sm from statsmodels.datasets.macrodata import data dta = data.load() gdp = np.log(dta.data['realgdp']) from numpy import polynomial from scipy import special maxorder = 20 polybase = special.chebyt polybase = special.legendre t = np.linspace(-1,1,len(gdp)) exog = np.column_stack([polybase(i)(t) for i in range(maxorder)]) fitted = [sm.OLS(gdp, exog[:, :maxr]).fit().fittedvalues for maxr in range(2,maxorder)] print((np.corrcoef(exog[:,1:6], rowvar=0)*10000).astype(int)) import matplotlib.pyplot as plt
from __future__ import print_function from .diagnostic import unitroot_adf import statsmodels.datasets.macrodata.data as macro macrod = macro.load().data print(macro.NOTE) print(macrod.dtype.names) datatrendli = [('realgdp', 1), ('realcons', 1), ('realinv', 1), ('realgovt', 1), ('realdpi', 1), ('cpi', 1), ('m1', 1), ('tbilrate', 0), ('unemp', 0), ('pop', 1), ('infl', 0), ('realint', 0)] print('%-10s %5s %-8s' % ('variable', 'trend', ' adf')) for name, torder in datatrendli: adf_, pval = unitroot_adf(macrod[name], trendorder=torder)[:2] print('%-10s %5d %8.4f %8.4f' % (name, torder, adf_, pval))
from __future__ import print_function from .diagnostic import unitroot_adf import statsmodels.datasets.macrodata.data as macro macrod = macro.load().data print(macro.NOTE) print(macrod.dtype.names) datatrendli = [ ('realgdp', 1), ('realcons', 1), ('realinv', 1), ('realgovt', 1), ('realdpi', 1), ('cpi', 1), ('m1', 1), ('tbilrate', 0), ('unemp',0), ('pop', 1), ('infl',0), ('realint', 0) ] print('%-10s %5s %-8s' % ('variable', 'trend', ' adf')) for name, torder in datatrendli: adf_, pval = unitroot_adf(macrod[name], trendorder=torder)[:2] print('%-10s %5d %8.4f %8.4f' % (name, torder, adf_, pval))
from statsmodels.tsa.stattools import adfuller import statsmodels.datasets.macrodata.data as macro macrod = macro.load(as_pandas=False).data print(macro.NOTE) print(macrod.dtype.names) datatrendli = [('realgdp', 1), ('realcons', 1), ('realinv', 1), ('realgovt', 1), ('realdpi', 1), ('cpi', 1), ('m1', 1), ('tbilrate', 0), ('unemp', 0), ('pop', 1), ('infl', 0), ('realint', 0)] print('%-10s %5s %-8s' % ('variable', 'trend', ' adf')) for name, torder in datatrendli: c_order = {0: "nc", 1: "c"} adf_, pval = adfuller(macrod[name], regression=c_order[torder])[:2] print('%-10s %5d %8.4f %8.4f' % (name, torder, adf_, pval))
from __future__ import print_function import numpy as np #import statsmodels.linear_model.regression as smreg from scipy import special import statsmodels.api as sm from statsmodels.datasets.macrodata import data dta = data.load(as_pandas=False) gdp = np.log(dta.data['realgdp']) maxorder = 20 polybase = special.chebyt polybase = special.legendre t = np.linspace(-1,1,len(gdp)) exog = np.column_stack([polybase(i)(t) for i in range(maxorder)]) fitted = [sm.OLS(gdp, exog[:, :maxr]).fit().fittedvalues for maxr in range(2,maxorder)] print((np.corrcoef(exog[:,1:6], rowvar=0)*10000).astype(int)) import matplotlib.pyplot as plt plt.figure() plt.plot(gdp, 'o')
from __future__ import print_function import numpy as np #import statsmodels.linear_model.regression as smreg from scipy import special import statsmodels.api as sm from statsmodels.datasets.macrodata import data dta = data.load(as_pandas=False) gdp = np.log(dta.data['realgdp']) from numpy import polynomial from scipy import special maxorder = 20 polybase = special.chebyt polybase = special.legendre t = np.linspace(-1, 1, len(gdp)) exog = np.column_stack([polybase(i)(t) for i in range(maxorder)]) fitted = [ sm.OLS(gdp, exog[:, :maxr]).fit().fittedvalues for maxr in range(2, maxorder) ] print((np.corrcoef(exog[:, 1:6], rowvar=0) * 10000).astype(int)) import matplotlib.pyplot as plt
import numpy as np #import statsmodels.linear_model.regression as smreg from scipy import special import statsmodels.api as sm from statsmodels.datasets.macrodata import data dta = data.load() gdp = np.log(dta.data['realgdp']) from numpy import polynomial from scipy import special maxorder = 20 polybase = special.chebyt polybase = special.legendre t = np.linspace(-1, 1, len(gdp)) exog = np.column_stack([polybase(i)(t) for i in range(maxorder)]) fitted = [ sm.OLS(gdp, exog[:, :maxr]).fit().fittedvalues for maxr in range(2, maxorder) ] print(np.corrcoef(exog[:, 1:6], rowvar=0) * 10000).astype(int) import matplotlib.pyplot as plt plt.figure()