approximate_factors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce43c153",
   "metadata": {},
   "source": [
    "# Approximate Factor Models\n",
    "\n",
    "_It is better to be vaguely right than precisely wrong_ - Carveth Read\n",
    "\n",
    "- Integration Order\n",
    "- Approximate factor model\n",
    "\n",
    "\n",
    "References:\n",
    "\n",
    "- Bai, Jushan and Ng, Serena, 2002, Determining the Number of Factors in Approximate Factor Models, Econometrica 70:1, 191-2211\n",
    "\n",
    "- McCracken, Michael W., and Serena Ng, 2016. FRED-MD: A monthly database for macroeconomic research, Journal of Business & Economic Statistics, 34(4), 574-589.\n",
    "\n",
    "- Michael W. McCracken, Serena Ng, 2020, FRED-QD: A Quarterly Database for Macroeconomic Research.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "788961be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from finds.readers import Alfred, fred_md, fred_qd\n",
    "from finds.recipes import (approximate_factors, mrsq, select_baing, integration_order,\n",
    "                           remove_outliers, is_outlier)\n",
    "from secret import credentials\n",
    "VERBOSE = 0\n",
    "#%matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73f11756",
   "metadata": {},
   "outputs": [],
   "source": [
    "alf = Alfred(api_key=credentials['fred']['api_key'], verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "757356d2",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start</th>\n",
       "      <th>End</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1854-12-31</td>\n",
       "      <td>1854-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1857-06-30</td>\n",
       "      <td>1858-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1860-10-31</td>\n",
       "      <td>1861-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1865-04-30</td>\n",
       "      <td>1867-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1869-06-30</td>\n",
       "      <td>1870-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1873-10-31</td>\n",
       "      <td>1879-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1882-03-31</td>\n",
       "      <td>1885-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1887-03-31</td>\n",
       "      <td>1888-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1890-07-31</td>\n",
       "      <td>1891-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1893-01-31</td>\n",
       "      <td>1894-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1895-12-31</td>\n",
       "      <td>1897-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1899-06-30</td>\n",
       "      <td>1900-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1902-09-30</td>\n",
       "      <td>1904-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1907-05-31</td>\n",
       "      <td>1908-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1910-01-31</td>\n",
       "      <td>1912-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1913-01-31</td>\n",
       "      <td>1914-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1918-08-31</td>\n",
       "      <td>1919-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1920-01-31</td>\n",
       "      <td>1921-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1923-05-31</td>\n",
       "      <td>1924-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1926-10-31</td>\n",
       "      <td>1927-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1929-08-31</td>\n",
       "      <td>1933-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1937-05-31</td>\n",
       "      <td>1938-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1945-02-28</td>\n",
       "      <td>1945-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1948-11-30</td>\n",
       "      <td>1949-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1953-07-31</td>\n",
       "      <td>1954-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1957-08-31</td>\n",
       "      <td>1958-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1960-04-30</td>\n",
       "      <td>1961-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1969-12-31</td>\n",
       "      <td>1970-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1973-11-30</td>\n",
       "      <td>1975-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1980-01-31</td>\n",
       "      <td>1980-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1981-07-31</td>\n",
       "      <td>1982-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1990-07-31</td>\n",
       "      <td>1991-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2001-03-31</td>\n",
       "      <td>2001-11-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2007-12-31</td>\n",
       "      <td>2009-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>2020-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Start        End\n",
       "0  1854-12-31 1854-12-31\n",
       "1  1857-06-30 1858-12-31\n",
       "2  1860-10-31 1861-06-30\n",
       "3  1865-04-30 1867-12-31\n",
       "4  1869-06-30 1870-12-31\n",
       "5  1873-10-31 1879-03-31\n",
       "6  1882-03-31 1885-05-31\n",
       "7  1887-03-31 1888-04-30\n",
       "8  1890-07-31 1891-05-31\n",
       "9  1893-01-31 1894-06-30\n",
       "10 1895-12-31 1897-06-30\n",
       "11 1899-06-30 1900-12-31\n",
       "12 1902-09-30 1904-08-31\n",
       "13 1907-05-31 1908-06-30\n",
       "14 1910-01-31 1912-01-31\n",
       "15 1913-01-31 1914-12-31\n",
       "16 1918-08-31 1919-03-31\n",
       "17 1920-01-31 1921-07-31\n",
       "18 1923-05-31 1924-07-31\n",
       "19 1926-10-31 1927-11-30\n",
       "20 1929-08-31 1933-03-31\n",
       "21 1937-05-31 1938-06-30\n",
       "22 1945-02-28 1945-10-31\n",
       "23 1948-11-30 1949-10-31\n",
       "24 1953-07-31 1954-05-31\n",
       "25 1957-08-31 1958-04-30\n",
       "26 1960-04-30 1961-02-28\n",
       "27 1969-12-31 1970-11-30\n",
       "28 1973-11-30 1975-03-31\n",
       "29 1980-01-31 1980-07-31\n",
       "30 1981-07-31 1982-11-30\n",
       "31 1990-07-31 1991-03-31\n",
       "32 2001-03-31 2001-11-30\n",
       "33 2007-12-31 2009-06-30\n",
       "34 2020-02-29 2020-04-30"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Retrieve recession periods from FRED\n",
    "vspans = alf.date_spans('USREC')\n",
    "DataFrame(vspans, columns=['Start', 'End'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b71e03",
   "metadata": {},
   "source": [
    "## Integration order\n",
    "\n",
    "### Augmented Dickey-Fuller test\n",
    "\n",
    "The Augmented Dickey-Fuller (ADF) specification is the most widely used unit root test. An ADF test is implemented using an OLS regression where the difference of a series is regressed on its lagged level, relevant deterministic terms, and lagged differ-ences. The general form of an ADF regression is\n",
    "\n",
    "$$\\Delta Y_t = \\gamma Y_{t-1} + (\\delta_o + \\delta_1 t) +  \\lambda_1 \\Delta Y_{t-1} +  ... + \\lambda_p \\Delta Y_{t-p}$$\n",
    "\n",
    "The balue of $\\gamma$ is 0 when $Y_t$ is a random walk, and so is non-stationary.  The alternative is $\\gamma < 0$ which corresponds to the case that $Y_t$ is covariance-stationary.  Note that the alternative is one-sided, since positive values of $\\gamma$ correspond to an AR coefficient that is larger than 1.\n",
    "\n",
    "When the null of a unit root cannot be rejected, the series should be differenced. The best practice is to repeat the ADF test on the iteratively-differenced data to ensure that it is stationary. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef9cd454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED-QD vintage: quarterly/current.csv\n",
      "FRED-MD vintage: monthly/current.csv\n"
     ]
    }
   ],
   "source": [
    "qd_df, qd_codes = fred_qd() # 202004\n",
    "md_df, md_codes = fred_md() # 201505\n",
    "qd_date = max(qd_df.index)\n",
    "md_date = max(md_df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a81ca8",
   "metadata": {},
   "source": [
    "### Transformations\n",
    "\n",
    "FRED-MD and FRED-QD series are tagged by one of seven transformation codes, which suggest transformations, such as taking logs or differences, to apply to the underlying series.  The number of times differences are taken of a series should remove unit roots and transform the series to be covariance stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "690f4261",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of series by suggested tcode transformations (20240331):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "      <th>log</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>fred-qd</th>\n",
       "      <th>fred-md</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       diff  log  pct_change  fred-qd  fred-md\n",
       "tcode                                         \n",
       "1         0    0           0       22       11\n",
       "2         1    0           0       32       19\n",
       "3         2    0           0        0        0\n",
       "4         0    1           0        0       10\n",
       "5         1    1           0      140       52\n",
       "6         2    1           0       50       33\n",
       "7         1    0           1        1        1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Number of series by suggested tcode transformations ({md_date}):\")\n",
    "tcodes = DataFrame.from_dict({i: alf.tcode[i] for i in range(1, 8)},\n",
    "                             orient='index')\\\n",
    "                  .join(qd_codes['transform'].value_counts().rename('fred-qd'))\\\n",
    "                  .join(md_codes['transform'].value_counts().rename('fred-md'))\\\n",
    "                  .fillna(0)\\\n",
    "                  .astype(int)\\\n",
    "                  .rename_axis(index='tcode')\n",
    "tcodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53e3e9a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each series, compare fitted integration order with tcode\n",
    "out = {}\n",
    "stationary_out = {}\n",
    "for label, df, transforms in [['md', md_df, md_codes['transform']],\n",
    "                              ['qd', qd_df, qd_codes['transform']]]:\n",
    "    stationary = dict()\n",
    "    for series_id, tcode in transforms.items():\n",
    "        # apply transformation if series tcode is valid\n",
    "        if tcode in range(1, 8):\n",
    "            # take logs of series if specified by tcode\n",
    "            s = np.log(df[series_id]) if tcode in [4, 5, 6] else df[series_id]\n",
    "\n",
    "            # estimate integration order\n",
    "            order = integration_order(s.dropna(), pvalue=0.05)\n",
    "\n",
    "            # expected order specified by tcode\n",
    "            expected_order = 2 if tcode == 7 else ((tcode - 1) % 3)\n",
    "\n",
    "            # accumulate results for this series\n",
    "            stationary[series_id] = {'tcode': tcode,\n",
    "                                     'I(p)': order,\n",
    "                                     'different': order - expected_order,\n",
    "                                     'title': alf.header(series_id)}\n",
    "#            print(series_id, tcode, expected_order, order)\n",
    "\n",
    "    # collect results for display\n",
    "    stationary = DataFrame.from_dict(stationary, orient='index')\n",
    "    stationary = stationary.sort_values(stationary.columns.to_list())\n",
    "    c = stationary.groupby(['tcode','I(p)'])['title'].count().reset_index()\n",
    "    out[label] = c.pivot(index='tcode', columns='I(p)', values='title')\\\n",
    "                  .fillna(0).astype(int)\n",
    "    out[label].columns=[f\"I({p})\" for p in out[label].columns]\n",
    "    stationary_out[label] = stationary[stationary['different'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd7540b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series by tcode, transformations and estimated order of integration:\n",
      "Integration order by transformation\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff</th>\n",
       "      <th>log</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>fred-qd</th>\n",
       "      <th>I(0)</th>\n",
       "      <th>I(1)</th>\n",
       "      <th>I(2)</th>\n",
       "      <th>fred-md</th>\n",
       "      <th>I(0)</th>\n",
       "      <th>I(1)</th>\n",
       "      <th>I(2)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>30</td>\n",
       "      <td>106</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       diff  log  pct_change  fred-qd  I(0)  I(1)  I(2)  fred-md  I(0)  I(1)  \\\n",
       "tcode                                                                          \n",
       "1         0    0           0       22    18     4     0       11    11     0   \n",
       "2         1    0           0       32    11    19     2       19     4    15   \n",
       "3         2    0           0        0     0     0     0        0     0     0   \n",
       "4         0    1           0        0     0     0     0       10     6     4   \n",
       "5         1    1           0      140    30   106     4       52    14    38   \n",
       "6         2    1           0       50     0    30    20       33     0    30   \n",
       "7         1    0           1        1     0     1     0        1     0     1   \n",
       "\n",
       "       I(2)  \n",
       "tcode        \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         3  \n",
       "7         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Series by tcode, transformations and estimated order of integration:')\n",
    "results = pd.concat([tcodes.drop(columns='fred-md'),\n",
    "                     out['qd'],\n",
    "                     tcodes['fred-md'],\n",
    "                     out['md']], axis=1).fillna(0).astype(int)\n",
    "print('Integration order by transformation')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "373cea8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED-MD Series with unit root after transformation\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tcode</th>\n",
       "      <th>I(p)</th>\n",
       "      <th>different</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PERMITMW</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>New Privately-Owned Housing Units Authorized i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERMITNE</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>New Privately-Owned Housing Units Authorized i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSTMW</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>New Privately-Owned Housing Units Started: Tot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSTNE</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>New Privately-Owned Housing Units Started: Tot...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          tcode  I(p)  different  \\\n",
       "PERMITMW      4     1          1   \n",
       "PERMITNE      4     1          1   \n",
       "HOUSTMW       4     1          1   \n",
       "HOUSTNE       4     1          1   \n",
       "\n",
       "                                                      title  \n",
       "PERMITMW  New Privately-Owned Housing Units Authorized i...  \n",
       "PERMITNE  New Privately-Owned Housing Units Authorized i...  \n",
       "HOUSTMW   New Privately-Owned Housing Units Started: Tot...  \n",
       "HOUSTNE   New Privately-Owned Housing Units Started: Tot...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('FRED-MD Series with unit root after transformation')\n",
    "stationary_out['md']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05f41084",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED-QD Series with unit root after transformation\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tcode</th>\n",
       "      <th>I(p)</th>\n",
       "      <th>different</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NWPI</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>*** NWPI ***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLBSNNBBDI</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>*** TLBSNNBBDI ***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLBSNNCBBDI</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>*** TLBSNNCBBDI ***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HWI</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Help Wanted Index for United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFDEBTN</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Federal Debt: Total Public Debt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S&amp;P div yield</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>S&amp;P's Composite Common Stock: Dividend Yield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CES2000000008</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Average Hourly Earnings of Production and Nons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TLBSHNO</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Households and Nonprofit Organizations; Total ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPHMFG</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Manufacturing Sector: Labor Productivity (Outp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPCS20RSA</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>S&amp;P CoreLogic Case-Shiller 20-City Composite H...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tcode  I(p)  different  \\\n",
       "NWPI               1     1          1   \n",
       "TLBSNNBBDI         1     1          1   \n",
       "TLBSNNCBBDI        1     1          1   \n",
       "HWI                1     1          1   \n",
       "GFDEBTN            2     2          1   \n",
       "S&P div yield      2     2          1   \n",
       "CES2000000008      5     2          1   \n",
       "TLBSHNO            5     2          1   \n",
       "OPHMFG             5     2          1   \n",
       "SPCS20RSA          5     2          1   \n",
       "\n",
       "                                                           title  \n",
       "NWPI                                                *** NWPI ***  \n",
       "TLBSNNBBDI                                    *** TLBSNNBBDI ***  \n",
       "TLBSNNCBBDI                                  *** TLBSNNCBBDI ***  \n",
       "HWI                          Help Wanted Index for United States  \n",
       "GFDEBTN                          Federal Debt: Total Public Debt  \n",
       "S&P div yield       S&P's Composite Common Stock: Dividend Yield  \n",
       "CES2000000008  Average Hourly Earnings of Production and Nons...  \n",
       "TLBSHNO        Households and Nonprofit Organizations; Total ...  \n",
       "OPHMFG         Manufacturing Sector: Labor Productivity (Outp...  \n",
       "SPCS20RSA      S&P CoreLogic Case-Shiller 20-City Composite H...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('FRED-QD Series with unit root after transformation')\n",
    "stationary_out['qd']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7eed4bd",
   "metadata": {},
   "source": [
    "## Approximate factor model\n",
    "\n",
    "__Principal component analysis__ finds a low-dimensional representation of a data set that contains as much as possible of the variation of its $p$ features.\n",
    "Each of the dimensions found by PCA is a linear\n",
    "combination of the features. The first principal component of a set\n",
    "of features $X_1, X_2,..., X_p$ is the normalized linear combination\n",
    "of the features $Z_1 = \\phi_{11} X_1 + \\phi_{21} X_2 + ... + \\phi_{p1}                                \n",
    "X_p$ that has the largest variance. We can then iteratively find the second principal\n",
    "component $Z_2$ (the linear combination of features that has maximal\n",
    "variance out of all linear combinations that are uncorrelated with\n",
    "previously found components), third component $Z_3$, and so on.\n",
    "\n",
    "\n",
    "### BIC criterion\n",
    "\n",
    "Select number of components\n",
    "\n",
    "### Data imputation\n",
    "\n",
    "EM algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "923f1a6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED-QD vintage: quarterly/2020-04.csv\n",
      "FRED-MD vintage: monthly/2015-05.csv\n",
      "FRED-MD monthly series:\n",
      "         selected  variance explained     start       end  obs  series\n",
      "factors         8            0.484228  19600331  20141231  658     133\n",
      "Factor:1 Variance Explained=0.1625\n",
      "           series                                 description\n",
      "0.7426     USGOOD              All Employees, Goods-Producing\n",
      "0.7236     PAYEMS                All Employees, Total Nonfarm\n",
      "0.6999     MANEMP                All Employees, Manufacturing\n",
      "0.6563       NAPM                                *** NAPM ***\n",
      "0.6538  IPMANSICS  Industrial Production: Manufacturing (SIC)\n",
      "0.6510    DMANEMP                All Employees, Durable Goods\n",
      "0.6312     INDPRO          Industrial Production: Total Index\n",
      "0.6036    NAPMNOI                             *** NAPMNOI ***\n",
      "0.6024     NAPMPI                              *** NAPMPI ***\n",
      "0.5601     CUMFNS   Capacity Utilization: Manufacturing (SIC)\n",
      "Factor:2 Variance Explained=0.0705\n",
      "          series                                        description\n",
      "0.5960   T10YFFM  10-Year Treasury Constant Maturity Minus Feder...\n",
      "0.5881    AAAFFM  Moody's Seasoned Aaa Corporate Bond Minus Fede...\n",
      "0.5576    BAAFFM  Moody's Seasoned Baa Corporate Bond Minus Fede...\n",
      "0.5543    T5YFFM  5-Year Treasury Constant Maturity Minus Federa...\n",
      "0.4591  TB6SMFFM     6-Month Treasury Bill Minus Federal Funds Rate\n",
      "0.4569  TB3SMFFM     3-Month Treasury Bill Minus Federal Funds Rate\n",
      "0.4137    T1YFFM  1-Year Treasury Constant Maturity Minus Federa...\n",
      "0.2385  COMPAPFF            3-Month Commercial Paper Minus FEDFUNDS\n",
      "0.2098    BUSINV                         Total Business Inventories\n",
      "0.1803    M2REAL                                Real M2 Money Stock\n",
      "Factor:3 Variance Explained=0.0652\n",
      "                 series                                        description\n",
      "0.6899      CUSR0000SAC  Consumer Price Index for All Urban Consumers: ...\n",
      "0.6791  DNDGRG3M086SBEA  Personal consumption expenditures: Nondurable ...\n",
      "0.6448         CPIAUCSL  Consumer Price Index for All Urban Consumers: ...\n",
      "0.6157    CUSR0000SA0L5  Consumer Price Index for All Urban Consumers: ...\n",
      "0.5871    CUUR0000SA0L2  Consumer Price Index for All Urban Consumers: ...\n",
      "0.5680            PCEPI  Personal Consumption Expenditures: Chain-type ...\n",
      "0.5635         CPITRNSL  Consumer Price Index for All Urban Consumers: ...\n",
      "0.5241         CPIULFSL  Consumer Price Index for All Urban Consumers: ...\n",
      "0.4626           PPIFCG  Producer Price Index by Commodity for Finished...\n",
      "0.4539           PPIITM  Producer Price Index by Commodity Intermediate...\n",
      "Factor:4 Variance Explained=0.0543\n",
      "        series                                        description\n",
      "0.4458     GS1  Market Yield on U.S. Treasury Securities at 1-...\n",
      "0.4383     GS5  Market Yield on U.S. Treasury Securities at 5-...\n",
      "0.4236     AAA          Moody's Seasoned Aaa Corporate Bond Yield\n",
      "0.4163   TB6MS  6-Month Treasury Bill Secondary Market Rate, D...\n",
      "0.4028    GS10  Market Yield on U.S. Treasury Securities at 10...\n",
      "0.3767     BAA          Moody's Seasoned Baa Corporate Bond Yield\n",
      "0.3340   TB3MS  3-Month Treasury Bill Secondary Market Rate, D...\n",
      "0.3257    CP3M        3-Month AA Financial Commercial Paper Rates\n",
      "0.2521   HOUST  New Privately-Owned Housing Units Started: Tot...\n",
      "0.2448  HOUSTW  New Privately-Owned Housing Units Started: Tot...\n",
      "Factor:5 Variance Explained=0.0428\n",
      "          series                                        description\n",
      "0.2489   PERMITW  New Privately-Owned Housing Units Authorized i...\n",
      "0.2380    PERMIT  New Privately-Owned Housing Units Authorized i...\n",
      "0.2325    HOUSTW  New Privately-Owned Housing Units Started: Tot...\n",
      "0.2199       GS5  Market Yield on U.S. Treasury Securities at 5-...\n",
      "0.2193       GS1  Market Yield on U.S. Treasury Securities at 1-...\n",
      "0.2155     HOUST  New Privately-Owned Housing Units Started: Tot...\n",
      "0.2005      GS10  Market Yield on U.S. Treasury Securities at 10...\n",
      "0.1971    T1YFFM  1-Year Treasury Constant Maturity Minus Federa...\n",
      "0.1963  PERMITMW  New Privately-Owned Housing Units Authorized i...\n",
      "0.1904     TB6MS  6-Month Treasury Bill Secondary Market Rate, D...\n",
      "Factor:6 Variance Explained=0.0364\n",
      "          series                                        description\n",
      "0.2481   IPCONGD              Industrial Production: Consumer Goods\n",
      "0.1904    NAPMEI                                     *** NAPMEI ***\n",
      "0.1831  IPDCONGD      Industrial Production: Durable Consumer Goods\n",
      "0.1820   ISRATIO         Total Business: Inventories to Sales Ratio\n",
      "0.1784   IPFINAL              Industrial Production: Final Products\n",
      "0.1547   IPFPNSS  Industrial Production: Final Products and Noni...\n",
      "0.1538    NAPMII                                     *** NAPMII ***\n",
      "0.1526      NAPM                                       *** NAPM ***\n",
      "0.1436  TB6SMFFM     6-Month Treasury Bill Minus Federal Funds Rate\n",
      "0.1419    AWHMAN  Average Weekly Hours of Production and Nonsupe...\n",
      "Factor:7 Variance Explained=0.0266\n",
      "                 series                                        description\n",
      "0.4509         TWEXMMTH  Nominal Major Currencies U.S. Dollar Index (Go...\n",
      "0.2293           EXSZUS     Swiss Francs to U.S. Dollar Spot Exchange Rate\n",
      "0.1660           EXUSUK  U.S. Dollars to U.K. Pound Sterling Spot Excha...\n",
      "0.1656           EXJPUS     Japanese Yen to U.S. Dollar Spot Exchange Rate\n",
      "0.1482           SRVPRD                   All Employees, Service-Providing\n",
      "0.1286    CES3000000008  Average Hourly Earnings of Production and Nons...\n",
      "0.1228    CES0600000008  Average Hourly Earnings of Production and Nons...\n",
      "0.1199          USTRADE                        All Employees, Retail Trade\n",
      "0.1042  DPCERA3M086SBEA  Real personal consumption expenditures (chain-...\n",
      "0.1026          UMCSENT         University of Michigan: Consumer Sentiment\n",
      "Factor:8 Variance Explained=0.0259\n",
      "               series                                        description\n",
      "0.3414        S&P 500          S&P's Common Stock Price Index: Composite\n",
      "0.2753  S&P div yield       S&P's Composite Common Stock: Dividend Yield\n",
      "0.2028   S&P PE ratio  S&P's Composite Common Stock: Price-Earnings R...\n",
      "0.1817         EXCAUS  Canadian Dollars to U.S. Dollar Spot Exchange ...\n",
      "0.1222        UMCSENT         University of Michigan: Consumer Sentiment\n",
      "0.1175       TWEXMMTH  Nominal Major Currencies U.S. Dollar Index (Go...\n",
      "0.1042      NONBORRES   Reserves of Depository Institutions, Nonborrowed\n",
      "0.0986       IPNCONGD  Industrial Production: Non-Durable Consumer Goods\n",
      "0.0950        IPCONGD              Industrial Production: Consumer Goods\n",
      "0.0828          AMBSL    St. Louis Adjusted Monetary Base (DISCONTINUED)\n",
      "FRED-QD quarterly series:\n",
      "         selected  variance explained     start       end  obs  series\n",
      "factors         7            0.497416  19600331  20191231  240     247\n",
      "Factor:1 Variance Explained=0.2018\n",
      "           series                                        description\n",
      "0.8403     USPRIV                       All Employees, Total Private\n",
      "0.8208     USGOOD                     All Employees, Goods-Producing\n",
      "0.8142     PAYEMS                       All Employees, Total Nonfarm\n",
      "0.8122  IPMANSICS         Industrial Production: Manufacturing (SIC)\n",
      "0.8109      OUTMS  Manufacturing Sector: Real Sectoral Output for...\n",
      "0.8058     INDPRO                 Industrial Production: Total Index\n",
      "0.7781     MANEMP                       All Employees, Manufacturing\n",
      "0.7722     HOANBS  Nonfarm Business Sector: Hours Worked for All ...\n",
      "0.7693    DMANEMP                       All Employees, Durable Goods\n",
      "0.7661     UNRATE                                  Unemployment Rate\n",
      "Factor:2 Variance Explained=0.0818\n",
      "          series                                        description\n",
      "0.5122    AAAFFM  Moody's Seasoned Aaa Corporate Bond Minus Fede...\n",
      "0.4907    T5YFFM  5-Year Treasury Constant Maturity Minus Federa...\n",
      "0.4545    PERMIT  New Privately-Owned Housing Units Authorized i...\n",
      "0.4419    BUSINV                         Total Business Inventories\n",
      "0.4180     HOUST  New Privately-Owned Housing Units Started: Tot...\n",
      "0.4001   PERMITS  New Privately-Owned Housing Units Authorized i...\n",
      "0.3814       TCU                  Capacity Utilization: Total Index\n",
      "0.3755   MZMREAL                Real MZM Money Stock (DISCONTINUED)\n",
      "0.3733  TB3SMFFM     3-Month Treasury Bill Minus Federal Funds Rate\n",
      "0.3719  GS10TB3M                                   *** GS10TB3M ***\n",
      "Factor:3 Variance Explained=0.0730\n",
      "                 series                                        description\n",
      "0.7561    CUSR0000SA0L2  Consumer Price Index for All Urban Consumers: ...\n",
      "0.7420      CUSR0000SAC  Consumer Price Index for All Urban Consumers: ...\n",
      "0.7370  DGDSRG3Q086SBEA  Personal consumption expenditures: Goods (chai...\n",
      "0.7165          PCECTPI  Personal Consumption Expenditures: Chain-type ...\n",
      "0.7092         CPITRNSL  Consumer Price Index for All Urban Consumers: ...\n",
      "0.6989  DNDGRG3Q086SBEA  Personal consumption expenditures: Nondurable ...\n",
      "0.6733    CUSR0000SA0L5  Consumer Price Index for All Urban Consumers: ...\n",
      "0.6636         CPIAUCSL  Consumer Price Index for All Urban Consumers: ...\n",
      "0.6523          WPSID61  Producer Price Index by Commodity: Intermediat...\n",
      "0.6285           PPIIDC  Producer Price Index by Commodity: Industrial ...\n",
      "Factor:4 Variance Explained=0.0468\n",
      "               series                                        description\n",
      "0.3966          IMFSL    Institutional Money Market Funds (DISCONTINUED)\n",
      "0.3490  CES9093000001                    All Employees, Local Government\n",
      "0.3283  CES9092000001                    All Employees, State Government\n",
      "0.2499         USGOVT                          All Employees, Government\n",
      "0.2313        GFDEBTN                    Federal Debt: Total Public Debt\n",
      "0.2276        REVOLSL    Revolving Consumer Credit Owned and Securitized\n",
      "0.2274         EXUSEU            U.S. Dollars to Euro Spot Exchange Rate\n",
      "0.2194        COMPRMS  Manufacturing Sector: Real Hourly Compensation...\n",
      "0.2173         USSERV                      All Employees, Other Services\n",
      "0.2145           NWPI                                       *** NWPI ***\n",
      "Factor:5 Variance Explained=0.0376\n",
      "             series                                        description\n",
      "0.3689       OPHMFG  Manufacturing Sector: Labor Productivity (Outp...\n",
      "0.3003          HWI                Help Wanted Index for United States\n",
      "0.2973         NWPI                                       *** NWPI ***\n",
      "0.2960       AWHMAN  Average Weekly Hours of Production and Nonsupe...\n",
      "0.2725       OPHPBS  Business Sector: Labor Productivity (Output pe...\n",
      "0.2355       OPHNFB  Nonfarm Business Sector: Labor Productivity (O...\n",
      "0.2347     UNRATELT                                   *** UNRATELT ***\n",
      "0.2281      UNLPNBS  Nonfarm Business Sector: Unit Nonlabor Payment...\n",
      "0.2158       ULCMFG  Manufacturing Sector: Unit Labor Costs for All...\n",
      "0.1965  TLBSNNCBBDI                                *** TLBSNNCBBDI ***\n",
      "Factor:6 Variance Explained=0.0301\n",
      "                 series                                        description\n",
      "0.2739            ULCBS  Business Sector: Unit Labor Costs for All Workers\n",
      "0.2704           ULCNFB  Nonfarm Business Sector: Unit Labor Costs for ...\n",
      "0.2515           CONSPI    Nonrevolving consumer credit to Personal Income\n",
      "0.2465           EXUSEU            U.S. Dollars to Euro Spot Exchange Rate\n",
      "0.2169           AHETPI  Average Hourly Earnings of Production and Nons...\n",
      "0.1946         CONSUMER               Consumer Loans, All Commercial Banks\n",
      "0.1775         NONREVSL  Nonrevolving Consumer Credit Owned and Securit...\n",
      "0.1480          TOTALSL        Total Consumer Credit Owned and Securitized\n",
      "0.1417          UMCSENT         University of Michigan: Consumer Sentiment\n",
      "0.1399  B021RE1Q156NBEA  Shares of gross domestic product: Imports of g...\n",
      "Factor:7 Variance Explained=0.0264\n",
      "            series                                        description\n",
      "0.2817  USEPUINDXM  Economic Policy Uncertainty Index for United S...\n",
      "0.2304    TNWBSHNO  Households and Nonprofit Organizations; Net Wo...\n",
      "0.2199     TABSHNO  Households and Nonprofit Organizations; Total ...\n",
      "0.2087   TFAABSHNO  Households and Nonprofit Organizations; Total ...\n",
      "0.2081      TARESA                                     *** TARESA ***\n",
      "0.2028     S&P 500          S&P's Common Stock Price Index: Composite\n",
      "0.1928   NASDAQCOM                             NASDAQ Composite Index\n",
      "0.1498    GS10TB3M                                   *** GS10TB3M ***\n",
      "0.1492      TB6M3M                                     *** TB6M3M ***\n",
      "0.1331    PCEPILFE  Personal Consumption Expenditures Excluding Fo...\n"
     ]
    }
   ],
   "source": [
    "# Verify BaiNg implemention on original FRED-MD and FRED-QD\n",
    "qd_df, qd_codes = fred_qd(202004)\n",
    "md_df, md_codes = fred_md(201505)\n",
    "for freq, df, transforms in [['monthly', md_df, md_codes['transform']],\n",
    "                             ['quarterly', qd_df, qd_codes['transform']]]:    \n",
    "    # Apply tcode transformations\n",
    "    transformed = []\n",
    "    for col in df.columns:\n",
    "        transformed.append(alf.transform(df[col],\n",
    "                                         tcode=transforms[col],\n",
    "                                         freq=freq[0]))\n",
    "    data = pd.concat(transformed, axis=1).iloc[2:]\n",
    "    cols = list(data.columns)\n",
    "    sample = data.index[((np.count_nonzero(np.isnan(data), axis=1)==0)\n",
    "                         | (data.index <= 20141231))\n",
    "                        & (data.index >= 19600301)]\n",
    "\n",
    "    # set missing and outliers to NaN\n",
    "    x = data.loc[sample]\n",
    "    x = remove_outliers(x)    # default fence 'iq10' is 10 times IQ \n",
    "\n",
    "    # compute factors EM and auto select number of components, r\n",
    "    Z = approximate_factors(x, p=2, verbose=VERBOSE)\n",
    "    r = select_baing(Z, p=2)\n",
    "\n",
    "    # show marginal R2's of series to each component\n",
    "    mR2 = mrsq(Z, r).to_numpy()\n",
    "    print(f\"FRED-{freq[0].upper()}D {freq} series:\")\n",
    "    print(DataFrame({'selected': r,\n",
    "                     'variance explained': np.sum(np.mean(mR2[:, :r], axis=0)),\n",
    "                     'start': min(sample),\n",
    "                     'end': max(sample),\n",
    "                     'obs': Z.shape[0],\n",
    "                     'series': Z.shape[1]},\n",
    "                    index=[f'factors']))\n",
    "\n",
    "    for k in range(r):\n",
    "        args = np.argsort(-mR2[:, k])\n",
    "        print(f\"Factor:{1+k} Variance Explained={np.mean(mR2[:,k]):.4f}\")\n",
    "        print(DataFrame.from_dict({mR2[arg, k].round(4):\n",
    "                                   {'series': cols[arg],\n",
    "                                    'description': alf.header(cols[arg])}\n",
    "                                   for arg in args[:10]},\n",
    "                                  orient='index'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f2a18ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED-MD vintage: monthly/current.csv\n"
     ]
    }
   ],
   "source": [
    "# Compute approximate factors from current FRED-MD\n",
    "data, t = fred_md()    # fetch dataframe of current fred-md and transform codes\n",
    "data.index = pd.to_datetime(data.index, format='%Y%m%d')\n",
    "transforms = t['transform']\n",
    "data = pd.concat([alf.transform(data[col], tcode=transforms[col], freq='m')\n",
    "                  for col in data.columns],  axis=1)\n",
    "\n",
    "# remove outliers and impute using Bai-Ng approach\n",
    "r = 8   # fix number of factors = 8\n",
    "X = remove_outliers(data, method='farout')\n",
    "X = approximate_factors(X, kmax=r, p=0, verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb28846",
   "metadata": {},
   "source": [
    "Recover factors as the projections from PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92b5b08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract factors from PCA projections on imputed data\n",
    "y = StandardScaler().fit_transform(X)\n",
    "pca = PCA(n_components=r).fit(y)\n",
    "factors = DataFrame(pca.transform(y), index=data.index, columns=range(1, 1+r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "511c2a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot extracted factors\n",
    "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(10, 12), sharex=True, sharey=True)\n",
    "axes = axes.flatten()\n",
    "for col, ax in zip(factors.columns, axes):\n",
    "    flip = -np.sign(max(factors[col]) + min(factors[col])) # try match sign\n",
    "    (flip*factors[col]).plot(ax=ax, color=f\"C{col}\")\n",
    "    for a,b in vspans:\n",
    "        if b >= min(factors.index):\n",
    "            ax.axvspan(max(a, min(factors.index)),\n",
    "                       min(b, max(factors.index)),\n",
    "                       alpha=0.4,\n",
    "                       color='grey')\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.legend([f\"Factor {col} Estimate\", 'NBER Recession'])\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.suptitle(f\"Factor Estimates {factors.index[0]:%b-%Y}:\"\n",
    "             f\"{factors.index[-1]:%b-%Y}\", fontsize='x-large')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "env3.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}