`freq` incompatible with `ndarray`

VECM_using_freq.ipynb

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   "source": [
    "from statsmodels.tsa.vecm.vecm import *\n",
    "import statsmodels.datasets.interest_inflation.data as d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "df = d.load_pandas().data\n",
    "data = np.array(df[[\"Dp\", \"R\"]])  # plain ndarray (doesn't have an index)"
   ]
  },
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    {
     "ename": "ValueError",
     "evalue": "Frequency provided without associated index.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-d3b8cf83cdca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m model = VECM(data, deterministic=\"ci\", freq=\"Q\", # seasons=4,\n\u001b[1;32m----> 2\u001b[1;33m              k_ar_diff=3, coint_rank=1)\n\u001b[0m",
      "\u001b[1;32m/home/vegcev/D/TUG/Masterarbeit/GSoC/statsmodels-yogabonito/statsmodels/tsa/vecm/vecm.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, exog_coint, dates, freq, k_ar_diff, coint_rank, deterministic, seasons, first_season)\u001b[0m\n\u001b[0;32m    814\u001b[0m                  \u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk_ar_diff\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoint_rank\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeterministic\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nc\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    815\u001b[0m                  seasons=0, first_season=0):  # rm seasons?\n\u001b[1;32m--> 816\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVECM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    817\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mexog_coint\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m                 \u001b[1;32mnot\u001b[0m \u001b[0mexog_coint\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mendog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/vegcev/D/TUG/Masterarbeit/GSoC/statsmodels-yogabonito/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, dates, freq, missing, **kwargs)\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[1;31m# Date handling in indexes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_dates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_init_dates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdates\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/vegcev/D/TUG/Masterarbeit/GSoC/statsmodels-yogabonito/statsmodels/tsa/base/tsa_model.py\u001b[0m in \u001b[0;36m_init_dates\u001b[1;34m(self, dates, freq)\u001b[0m\n\u001b[0;32m    102\u001b[0m         \u001b[1;31m# Sanity check that we don't have a `freq` without an index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Frequency provided without associated index.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m         \u001b[1;31m# If an index is available, see if it is a date-based index or if it\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Frequency provided without associated index."
     ]
    }
   ],
   "source": [
    "model = VECM(data, deterministic=\"ci\", freq=\"Q\", # seasons=4,\n",
    "             k_ar_diff=3, coint_rank=1)"
   ]
  },
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Since freq seems incompatible with ndarray it is probably better to pass the information about the number of seasons separately (via a seasons argument).