113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
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'''Paneldata model with fixed effect (constants) and AR(1) errors
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checking fast evaluation of groupar1filter
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quickly written to try out grouparfilter without python loops
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maybe the example has MA(1) not AR(1) errors, I'm not sure and changed this.
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results look good, I'm also differencing the dummy variable (constants) ???
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e.g. nobs = 35
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true 0.6, 10, 20, 30 (alpha, mean_0, mean_1, mean_2)
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estimate 0.369453125 [ 10.14646929 19.87135086 30.12706505]
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Currently minimizes ssr but could switch to minimize llf, i.e. conditional MLE.
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This should correspond to iterative FGLS, where data are AR(1) transformed
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similar to GLSAR ?
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Result statistic from GLS return by OLS on transformed data should be
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asymptotically correct (check)
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Could be extended to AR(p) errors, but then requires panel with larger T
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'''
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import numpy as np
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from scipy import optimize
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from statsmodels.regression.linear_model import OLS
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class PanelAR1:
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def __init__(self, endog, exog=None, groups=None):
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#take this from a super class, no checking is done here
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nobs = endog.shape[0]
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self.endog = endog
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if exog is not None:
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self.exog = exog
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self.groups_start = (np.diff(groups)!=0)
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self.groups_valid = ~self.groups_start
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def ar1filter(self, xy, alpha):
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#print(alpha,)
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return (xy[1:] - alpha * xy[:-1])[self.groups_valid]
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def fit_conditional(self, alpha):
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y = self.ar1filter(self.endog, alpha)
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x = self.ar1filter(self.exog, alpha)
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res = OLS(y, x).fit()
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return res.ssr #res.llf
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def fit(self):
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alpha0 = 0.1 #startvalue
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func = self.fit_conditional
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fitres = optimize.fmin(func, alpha0)
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# fit_conditional only returns ssr for now
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alpha = fitres[0]
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y = self.ar1filter(self.endog, alpha)
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x = self.ar1filter(self.exog, alpha)
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reso = OLS(y, x).fit()
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return fitres, reso
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if __name__ == '__main__':
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#------------ development code for groupar1filter and example
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groups = np.array([0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,
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2,2,2,2,2,2,2,2])
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nobs = len(groups)
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data0 = np.arange(nobs)
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data = np.arange(1,nobs+1) - 0.5*np.arange(nobs) + 0.1*np.random.randn(nobs)
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y00 = 0.5*np.random.randn(nobs+1)
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# I do not think a trend is handled yet
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data = np.arange(nobs) + y00[1:] + 0.2*y00[:-1] + 0.1*np.random.randn(nobs)
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#Are these AR(1) or MA(1) errors ???
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data = y00[1:] + 0.6*y00[:-1] #+ 0.1*np.random.randn(nobs)
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group_codes = np.unique(groups)
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group_dummy = (groups[:,None] == group_codes).astype(int)
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groups_start = (np.diff(groups)!=0)
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groups_valid = (np.diff(groups)==0) #this applies to y with length for AR(1)
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#could use np.nonzero for index instead
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y = data + np.dot(group_dummy, np.array([10, 20, 30]))
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y0 = data0 + np.dot(group_dummy, np.array([10, 20, 30]))
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print(groups_valid)
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print(np.diff(y)[groups_valid])
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alpha = 1 #test with 1
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print((y0[1:] - alpha*y0[:-1])[groups_valid])
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alpha = 0.2 #test with 1
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print((y0[1:] - alpha*y0[:-1] + 0.001)[groups_valid])
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#this is now AR(1) for each group separately
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#------------
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#fitting the example
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exog = np.ones(nobs)
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exog = group_dummy
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mod = PanelAR1(y, exog, groups=groups)
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#mod = PanelAR1(data, exog, groups=groups) #data does not contain different means
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#print(mod.ar1filter(mod.endog, 1))
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resa, reso = mod.fit()
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print(resa[0], reso.params)
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