def __init__(self, constant=True):
        self.avobs = 300.
        if constant:
            self.params = [ 6.7430535917332, 1.1649421971129,  -.40535742259304,
                      -.16653934246587,  .14980629416032,  -.09462417064796,
                       .00491001240749,  .0504665930841, -.08635349190816,
                       .25349103194757]
# These are returned by stata VAR, using the (V)AR scale/sigma
# we return the true OLS bse by default
# the stata residuals can be achived by np.sqrt(np.diag(res1.cov_params()))
            self.bse_stata = [2.413485601, .0560359041, .0874490762,
                              .0900894414, .0899348339, .0900100797,
                              .0898385666, .0896997939, .0869773089,
                              .0559505756]
# The below are grom gretl's ARIMA command with conditional maxium likelihood
            self.bse_gretl = [2.45474, 0.0569939, 0.0889440, 0.0916295,
                            0.0914723, 0.0915488, 0.0913744, 0.0912332,
                            0.0884642, 0.0569071]
            self.rmse = 15.1279294937327
            self.fpe = 236.4827257929261
            self.llf = -1235.559128419549
#NOTE: we use a different definition of these ic than Stata
# but our order selection results agree with R VARselect
# close to Stata for Lutkepohl but we penalize the ic for the trend terms
#            self.bic = 8.427186938618863
#            self.aic = 8.30372752279699
#            self.hqic = 8.353136159250697

#NOTE: predictions were taken from gretl, but agree with Stata
            # test predict
#TODO: remove one of the files
            filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                "AROLSConstantPredict.csv")
            predictresults = np.loadtxt(filename)
            fv = predictresults[:300,0]
            pv = predictresults[300:,1]
            pv_lb = predictresults[300:,2]
            pv_ub = predictresults[300:,3]
            pv_se = predictresults[300:,4]
            del predictresults

            # cases - in sample predict
            # n = -1, start = 0 (fitted values)
            self.FVOLSnneg1start0 = fv
            # n=-1, start=9
            self.FVOLSnneg1start9 = fv
            # n=-1, start=100
            self.FVOLSnneg1start100 = fv[100-9:]
            # n = 200, start = 0
            self.FVOLSn200start0 = fv[:192]
            # n = 200, start = 200
            self.FVOLSn200start200 = np.hstack((fv[200-9:],pv[:101-9]))
            # n = 200, start = -109 use above
            self.FVOLSn200startneg109 = self.FVOLSn200start200
            # n = 100, start = 325, post-sample forecasting
            self.FVOLSn100start325 = np.hstack((fv[-1],pv))
            # n = 301, start = 9
            self.FVOLSn301start9 = np.hstack((fv,pv[:2]))
            # n = 301, start = 0
            self.FVOLSdefault = fv
            # n = 4, start = 312
            self.FVOLSn4start312 = np.hstack((fv[-1],pv[:8]))
            # n = 15, start = 312
            self.FVOLSn15start312 = np.hstack((fv[-1],pv[:19]))


        elif not constant:
            self.params = [1.19582389902985, -0.40591818219637,
                           -0.15813796884843, 0.16620079925202,
                           -0.08570200254617, 0.01876298948686,
                           0.06130211910707, -0.08461507700047,
                           0.27995084653313]
            self.bse_stata = [.055645055, .088579237, .0912031179, .0909032462,
                    .0911161784, .0908611473, .0907743174, .0880993504,
                    .0558560278]
            self.bse_gretl = [0.0564990, 0.0899386, 0.0926027, 0.0922983,
                              0.0925145, 0.0922555, 0.0921674, 0.0894513,
                              0.0567132]
            self.rmse = 15.29712618677774
            self.sigma = 226.9820074869752
            self.llf = -1239.41217278661
# See note above
#            self.bic =  8.433861292817106
#            self.hqic =  8.367215591385756
#            self.aic =  8.322747818577421
            self.fpe =  241.0221316614273
            filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                "AROLSNoConstantPredict.csv")
            predictresults = np.loadtxt(filename)
            fv = predictresults[:300,0]
            pv = predictresults[300:,1]
            pv_lb = predictresults[300:,2]
            pv_ub = predictresults[300:,3]
            pv_se = predictresults[300:,4]
            del predictresults

            # cases - in sample predict
            # n = -1, start = 0 (fitted values)
            self.FVOLSnneg1start0 = fv
            # n=-1, start=9
            self.FVOLSnneg1start9 = fv
            # n=-1, start=100
            self.FVOLSnneg1start100 = fv[100-9:]
            # n = 200, start = 0
            self.FVOLSn200start0 = fv[:192]
            # n = 200, start = 200
            self.FVOLSn200start200 = np.hstack((fv[200-9:],pv[:101-9]))
            # n = 200, start = -109 use above
            self.FVOLSn200startneg109 = self.FVOLSn200start200
            # n = 100, start = 325, post-sample forecasting
            self.FVOLSn100start325 = np.hstack((fv[-1],pv))
            # n = 301, start = 9
            self.FVOLSn301start9 = np.hstack((fv,pv[:2]))
            # n = 301, start = 0
            self.FVOLSdefault = fv
            # n = 4, start = 312
            self.FVOLSn4start312 = np.hstack((fv[-1],pv[:8]))
            # n = 15, start = 312
            self.FVOLSn15start312 = np.hstack((fv[-1],pv[:19]))
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/tests/results/results_ar.py/ARResultsOLS.__init__/original