tsa_arma.ipynb

{
 "metadata": {
  "name": "tsa_arma"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "ARMA example using sunpots data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from scipy import stats\n",
      "import pandas\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import statsmodels.api as sm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.graphics.api import qqplot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sm.datasets.sunspots.NOTE"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dta = sm.datasets.sunspots.load_pandas().data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))\n",
      "del dta[\"YEAR\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dta.plot(figsize=(12,8));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax1 = fig.add_subplot(211)\n",
      "fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n",
      "ax2 = fig.add_subplot(212)\n",
      "fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()\n",
      "print arma_mod20.params"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma_mod30 = sm.tsa.ARMA(dta, (3,0)).fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arma_mod30.params"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Does our model obey the theory?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.stats.durbin_watson(arma_mod30.resid.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax = fig.add_subplot(111)\n",
      "ax = arma_mod30.resid.plot(ax=ax);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resid = arma_mod30.resid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.normaltest(resid)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax = fig.add_subplot(111)\n",
      "fig = qqplot(resid, line='q', ax=ax, fit=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax1 = fig.add_subplot(211)\n",
      "fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)\n",
      "ax2 = fig.add_subplot(212)\n",
      "fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n",
      "data = np.c_[range(1,41), r[1:], q, p]\n",
      "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
      "print table.set_index('lag')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* This indicates a lack of fit."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* In-sample dynamic prediction. How good does our model do?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)\n",
      "print predict_sunspots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = dta.ix['1950':].plot(figsize=(12,8))\n",
      "ax = predict_sunspots.plot(ax=ax, style='r--', label='Dynamic Prediction');\n",
      "ax.legend();\n",
      "ax.axis((-20.0, 38.0, -4.0, 200.0));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mean_forecast_err(y, yhat):\n",
      "    return y.sub(yhat).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise: Can you obtain a better fit for the Sunspots model? (Hint: sm.tsa.AR has a method select_order)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Simulated ARMA(4,1): Model Identification is Difficult"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(1234)\n",
      "# include zero-th lag\n",
      "arparams = np.array([1, .75, -.65, -.55, .9])\n",
      "maparams = np.array([1, .65])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Let's make sure this models is estimable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma_t = ArmaProcess(arparams, maparams)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma_t.isinvertible()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma_t.isstationary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "* What does this mean?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax = fig.add_subplot(111)\n",
      "ax.plot(arma_t.generate_sample(size=50));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arparams = np.array([1, .35, -.15, .55, .1])\n",
      "maparams = np.array([1, .65])\n",
      "arma_t = ArmaProcess(arparams, maparams)\n",
      "arma_t.isstationary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma_rvs = arma_t.generate_sample(size=500, burnin=250, scale=2.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax1 = fig.add_subplot(211)\n",
      "fig = sm.graphics.tsa.plot_acf(arma_rvs, lags=40, ax=ax1)\n",
      "ax2 = fig.add_subplot(212)\n",
      "fig = sm.graphics.tsa.plot_pacf(arma_rvs, lags=40, ax=ax2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "* For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags. \n",
      "* The partial autocorrelation function is a mixture of exponentials and dampened sine waves after (p-q) lags."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma11 = sm.tsa.ARMA(arma_rvs, (1,1)).fit()\n",
      "resid = arma11.resid\n",
      "r,q,p = sm.tsa.acf(resid, qstat=True)\n",
      "data = np.c_[range(1,41), r[1:], q, p]\n",
      "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
      "print table.set_index('lag')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arma41 = sm.tsa.ARMA(arma_rvs, (4,1)).fit()\n",
      "resid = arma41.resid\n",
      "r,q,p = sm.tsa.acf(resid, qstat=True)\n",
      "data = np.c_[range(1,41), r[1:], q, p]\n",
      "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
      "print table.set_index('lag')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exercise: How good of in-sample prediction can you do for another series, say, CPI"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "macrodta = sm.datasets.macrodata.load_pandas().data\n",
      "macrodta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n",
      "cpi = macrodta[\"cpi\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Hint: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "ax = fig.add_subplot(111)\n",
      "ax = cpi.plot(ax=ax);\n",
      "ax.legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "P-value of the unit-root test, resoundly rejects the null of no unit-root."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sm.tsa.adfuller(cpi)[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}