def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5): '''run Monte Carlo for ARMA(2,2) DGP parameters currently hard coded also sample size `nsample` was not a self contained function, used instances from outer scope now corrected ''' #nsample = 1000 #ar = [1.0, 0, 0] if ar is None: ar = [1.0, -0.55, -0.1] #ma = [1.0, 0, 0] if ma is None: ma = [1.0, 0.3, 0.2] results = [] results_bse = [] for _ in range(niter): y2 = arma_generate_sample(ar, ma, nsample + 1000, sig)[-nsample:] y2 -= y2.mean() arest2 = Arma(y2) rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2, 2)) results.append(rhohat2a) err2a = arest2.geterrors(rhohat2a) sige2a = np.sqrt(np.dot(err2a, err2a) / nsample) #print 'sige2a', sige2a, #print 'cov_x2a.shape', cov_x2a.shape #results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a))) if not cov_x2a is None: results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a))) else: results_bse.append(np.nan + np.zeros_like(rhohat2a)) return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse)
def setup_class(cls): nobs = 500 ar = [1, -0.5, 0.1] ma = [1, 0.7] dist = lambda n: np.random.standard_t(3, size=n) np.random.seed(8659567) x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist, burnin=500) mod = Arma(x) order = (2, 1) cls.res_ls = mod.fit(order=order) cls.res = mod.fit_mle(order=order, start_params=np.r_[cls.res_ls[0], 1], method='nm', disp=False) cls.res1_table = np.array( [[ 0.4339072 , -0.08402653, 0.73292344, 1.61661128], [ 0.05854268, 0.05562941, 0.04034178, 0.0511207 ], [ 7.4118102 , -1.51046975, 18.16785075, 31.62341666], [ 0. , 0.1309236 , 0. , 0. ], [ 0.06713617, 0.05469138, 0.03785006, 0.1071093 ], [ 0.05504093, 0.0574849 , 0.04350945, 0.02510928]]) cls.res1_conf_int = np.array([[ 0.31916567, 0.54864874], [-0.19305817, 0.0250051 ], [ 0.65385501, 0.81199188], [ 1.51641655, 1.71680602]]) cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058]) cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5): '''run Monte Carlo for ARMA(2,2) DGP parameters currently hard coded also sample size `nsample` was not a self contained function, used instances from outer scope now corrected ''' #nsample = 1000 #ar = [1.0, 0, 0] if ar is None: ar = [1.0, -0.55, -0.1] #ma = [1.0, 0, 0] if ma is None: ma = [1.0, 0.3, 0.2] results = [] results_bse = [] for _ in range(niter): y2 = arma_generate_sample(ar,ma,nsample+1000, sig)[-nsample:] y2 -= y2.mean() arest2 = Arma(y2) rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2,2)) results.append(rhohat2a) err2a = arest2.geterrors(rhohat2a) sige2a = np.sqrt(np.dot(err2a,err2a)/nsample) #print('sige2a', sige2a, #print('cov_x2a.shape', cov_x2a.shape #results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a))) if cov_x2a is not None: results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a))) else: results_bse.append(np.nan + np.zeros_like(rhohat2a)) return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse)
def test_compare_arma(): #this is a preliminary test to compare arma_kf, arma_cond_ls and arma_cond_mle #the results returned by the fit methods are incomplete #for now without random.seed #np.random.seed(9876565) x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200, burnin=1000) # this used kalman filter through descriptive # d = ARMA(x) # d.fit((1,1), trend='nc') # dres = d.res modkf = ARMA(x) ##rkf = mkf.fit((1,1)) ##rkf.params reskf = modkf.fit((1,1), trend='nc', disp=-1) dres = reskf modc = Arma(x) resls = modc.fit(order=(1,1)) rescm = modc.fit_mle(order=(1,1), start_params=[0.4,0.4, 1.], disp=0) #decimal 1 corresponds to threshold of 5% difference #still different sign corrcted #assert_almost_equal(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1) assert_almost_equal(resls[0] / dres.params, np.ones(dres.params.shape), decimal=1) #rescm also contains variance estimate as last element of params #assert_almost_equal(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1) assert_almost_equal(rescm.params[:-1] / dres.params, np.ones(dres.params.shape), decimal=1)
def setup_class(cls): nobs = 500 ar = [1, -0.5, 0.1] ma = [1, 0.7] dist = partial(np.random.standard_t, 3) np.random.seed(8659567) x = arma_generate_sample(ar, ma, nobs, scale=1, distrvs=dist, burnin=500) with pytest.warns(FutureWarning): mod = Arma(x) order = (2, 1) cls.res_ls = mod.fit(order=order) cls.res = mod.fit_mle( order=order, start_params=np.r_[cls.res_ls[0], 1], method="nm", disp=False, ) cls.res1_table = np.array([ [0.43390720, -0.08402653, 0.73292344, 1.61661128], [0.05854268, 0.055629410, 0.04034178, 0.05112070], [7.41181020, -1.51046975, 18.16785075, 31.62341666], [0.00000000, 0.130923600, 0.00000000, 0.00000000], [0.06713617, 0.054691380, 0.03785006, 0.10710930], [0.05504093, 0.057484900, 0.04350945, 0.02510928], ]) cls.res1_conf_int = np.array([ [0.31916567, 0.54864874], [-0.19305817, 0.02500510], [0.65385501, 0.81199188], [1.51641655, 1.71680602], ]) cls.ls_params = np.array([0.43393123, -0.08402678, 0.73293058]) cls.ls_bse = np.array([0.0377741, 0.03567847, 0.02744488])
def test_compare_arma(): #this is a preliminary test to compare arma_kf, arma_cond_ls and arma_cond_mle #the results returned by the fit methods are incomplete #for now without random.seed #np.random.seed(9876565) x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200, burnin=1000) # this used kalman filter through descriptive # d = ARMA(x) # d.fit((1,1), trend='nc') # dres = d.res modkf = ARMA(x) ##rkf = mkf.fit((1,1)) ##rkf.params reskf = modkf.fit((1, 1), trend='nc', disp=-1) dres = reskf modc = Arma(x) resls = modc.fit(order=(1, 1)) rescm = modc.fit_mle(order=(1, 1), start_params=[0.4, 0.4, 1.], disp=0) #decimal 1 corresponds to threshold of 5% difference #still different sign corrcted #assert_almost_equal(np.abs(resls[0] / d.params), np.ones(d.params.shape), decimal=1) assert_almost_equal(resls[0] / dres.params, np.ones(dres.params.shape), decimal=1) #rescm also contains variance estimate as last element of params #assert_almost_equal(np.abs(rescm.params[:-1] / d.params), np.ones(d.params.shape), decimal=1) assert_almost_equal(rescm.params[:-1] / dres.params, np.ones(dres.params.shape), decimal=1)
import statsmodels.api as sm from statsmodels.sandbox import tsa from statsmodels.tsa.arma_mle import Arma # local import from statsmodels.tsa.arima_process import arma_generate_sample examples = ['arma'] if 'arma' in examples: print("\nExample 1") print('----------') ar = [1.0, -0.8] ma = [1.0, 0.5] y1 = arma_generate_sample(ar, ma, 1000, 0.1) y1 -= y1.mean() #no mean correction/constant in estimation so far arma1 = Arma(y1) arma1.nar = 1 arma1.nma = 1 arma1res = arma1.fit_mle(order=(1, 1), method='fmin') print(arma1res.params) #Warning need new instance otherwise results carry over arma2 = Arma(y1) arma2.nar = 1 arma2.nma = 1 res2 = arma2.fit(method='bfgs') print(res2.params) print(res2.model.hessian(res2.params)) print(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)) arest = tsa.arima.ARIMA(y1) resls = arest.fit((1, 0, 1))
y22 = arma_generate_sample(ar, ma, nobs + 1000, 0.5)[-nobs:] y22 -= y22.mean() start_params = [0.1, 0.1, 0.1, 0.1] start_params_lhs = [-0.1, -0.1, 0.1, 0.1] print 'truelhs', np.r_[ar[1:], ma[1:]] ###bug in current version, fixed in Skipper and 1 more ###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs ###ValueError: array dimensions are not compatible for copy ##arma22 = ARMA_kf(y22, constant=False, order=(2,2)) ##res = arma22.fit(start_params=start_params) ##print res.params print '\nARIMA new' arest2 = Arma(y22) naryw = 4 #= 30 resyw = sm.regression.yule_walker(y22, order=naryw, inv=True) arest2.nar = naryw arest2.nma = 0 e = arest2.geterrors(np.r_[1, -resyw[0]]) x = sm.tsa.tsatools.lagmat2ds(np.column_stack((y22, e)), 3, dropex=1, trim='both') yt = x[:, 0] xt = x[:, 1:] res_ols = sm.OLS(yt, xt).fit() print 'hannan_rissannen' print res_ols.params
print('truelhs', np.r_[ar[1:], ma[1:]]) ###bug in current version, fixed in Skipper and 1 more ###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs ###ValueError: array dimensions are not compatible for copy ##from statsmodels.tsa.arima import ARMA as ARMA_kf ##arma22 = ARMA_kf(y22, constant=False, order=(2,2)) ##res = arma22.fit(start_params=start_params) ##print res.params print('\nARIMA new') arest2 = Arma(y22) naryw = 4 #= 30 resyw = sm.regression.yule_walker(y22, order=naryw, inv=True) arest2.nar = naryw arest2.nma = 0 e = arest2.geterrors(np.r_[1, -resyw[0]]) x=sm.tsa.tsatools.lagmat2ds(np.column_stack((y22,e)),3,dropex=1, trim='both') yt = x[:,0] xt = x[:,1:] res_ols = sm.OLS(yt, xt).fit() print('hannan_rissannen') print(res_ols.params) start_params = res_ols.params start_params_mle = np.r_[-res_ols.params[:2],
from __future__ import print_function from time import time from statsmodels.tsa.arma_mle import Arma from statsmodels.tsa.api import ARMA import numpy as np print("Battle of the dueling ARMAs") y_arma22 = np.loadtxt(r'C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\tsa\y_arma22.txt') arma1 = Arma(y_arma22) arma2 = ARMA(y_arma22) print("The actual results from gretl exact mle are") params_mle = np.array([.826990, -.333986, .0362419, -.792825]) sigma_mle = 1.094011 llf_mle = -1510.233 print("params: ", params_mle) print("sigma: ", sigma_mle) print("llf: ", llf_mle) print("The actual results from gretl css are") params_css = np.array([.824810, -.337077, .0407222, -.789792]) sigma_css = 1.095688 llf_css = -1507.301 results = [] results += ["gretl exact mle", params_mle, sigma_mle, llf_mle] results += ["gretl css", params_css, sigma_css, llf_css] t0 = time() print("Exact MLE - Kalman filter version using l_bfgs_b")
import numpy as np from numpy.testing import assert_almost_equal import matplotlib.pyplot as plt import statsmodels.sandbox.tsa.fftarma as fa from statsmodels.tsa.descriptivestats import TsaDescriptive from statsmodels.tsa.arma_mle import Arma x = fa.ArmaFft([1, -0.5], [1., 0.4], 40).generate_sample(size=200, burnin=1000) d = TsaDescriptive(x) d.plot4() #d.fit(order=(1,1)) d.fit((1,1), trend='nc') print(d.res.params) modc = Arma(x) resls = modc.fit(order=(1,1)) print(resls[0]) rescm = modc.fit_mle(order=(1,1), start_params=[-0.4,0.4, 1.]) print(rescm.params) #decimal 1 corresponds to threshold of 5% difference assert_almost_equal(resls[0] / d.res.params, 1, decimal=1) assert_almost_equal(rescm.params[:-1] / d.res.params, 1, decimal=1) #copied to tsa.tests plt.figure() plt.plot(x, 'b-o') plt.plot(modc.predicted(), 'r-') plt.figure() plt.plot(modc.error_estimate)
order = (2, 1) res = mod.fit(order=order) res2 = mod.fit_mle(order=order, start_params=np.r_[res[0], 5, 1], method='nm') print(res[0]) proc = ArmaProcess.from_coeffs(res[0][:order[0]], res[0][:order[1]]) print(ar, ma) proc.nobs = nobs # TODO: bug nobs is None, not needed ?, used in ArmaProcess.__repr__ print(proc.ar, proc.ma) print(proc.ar_roots(), proc.ma_roots()) from statsmodels.tsa.arma_mle import Arma modn = Arma(x) resn = modn.fit_mle(order=order) moda = ARMA(x, order=order) resa = moda.fit( trend='nc') print('\nparameter estimates') print('ls ', res[0]) print('norm', resn.params) print('t ', res2.params) print('A ', resa.params) print('\nstandard deviation of parameter estimates') #print 'ls ', res[0] #TODO: not available yet print('norm', resn.bse) print('t ', res2.bse)
from time import time from statsmodels.tsa.arma_mle import Arma from statsmodels.tsa.api import ARMA import numpy as np print("Battle of the dueling ARMAs") y_arma22 = np.loadtxt( r'C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\tsa\y_arma22.txt' ) arma1 = Arma(y_arma22) arma2 = ARMA(y_arma22) print("The actual results from gretl exact mle are") params_mle = np.array([.826990, -.333986, .0362419, -.792825]) sigma_mle = 1.094011 llf_mle = -1510.233 print("params: ", params_mle) print("sigma: ", sigma_mle) print("llf: ", llf_mle) print("The actual results from gretl css are") params_css = np.array([.824810, -.337077, .0407222, -.789792]) sigma_css = 1.095688 llf_css = -1507.301 results = [] results += ["gretl exact mle", params_mle, sigma_mle, llf_mle] results += ["gretl css", params_css, sigma_css, llf_css] t0 = time()
import statsmodels.api as sm from statsmodels.sandbox import tsa from statsmodels.tsa.arma_mle import Arma # local import from statsmodels.tsa.arima_process import arma_generate_sample examples = ['arma'] if 'arma' in examples: print "\nExample 1" print '----------' ar = [1.0, -0.8] ma = [1.0, 0.5] y1 = arma_generate_sample(ar,ma,1000,0.1) y1 -= y1.mean() #no mean correction/constant in estimation so far arma1 = Arma(y1) arma1.nar = 1 arma1.nma = 1 arma1res = arma1.fit_mle(order=(1,1), method='fmin') print arma1res.params #Warning need new instance otherwise results carry over arma2 = Arma(y1) arma2.nar = 1 arma2.nma = 1 res2 = arma2.fit(method='bfgs') print res2.params print res2.model.hessian(res2.params) print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params) arest = tsa.arima.ARIMA(y1) resls = arest.fit((1,0,1))