{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bda38497",
   "metadata": {},
   "source": [
    "## Hurdle and truncated count models\n",
    "\n",
    "Author: Josef Perktold\n",
    "\n",
    "Statsmodels has now hurdle and truncated count models, added in version 0.14.\n",
    "\n",
    "A hurdle model is composed of a model for zeros and a model for the distribution for counts larger than zero. The zero model is a binary model for a count of zero versus larger than zero. The count model for nonzero counts is a zero truncated count model.\n",
    "\n",
    "Statsmodels currently supports hurdle models with Poisson and Negative Binomial distributions as zero model and as count model. Binary models like Logit, Probit or GLM-Binomial are not yet supported as zero model.\n",
    "The advantage of Poisson-Poisson hurdle is that the standard Poisson model is a special case with equal parameters in both models. This provides a simple Wald test for the hurdle model against the Poisson model.\n",
    "\n",
    "The implemented binary model is a censored model where observations are right censored at one. That means that only 0 or 1 counts are observed.\n",
    "\n",
    "The hurdle model can be estimated by separately estimating the zero model and the count model for the zero truncated data assuming that observations are independently distributed (no correlation across observations). The resulting covariance matrix of the parameter estimates is block diagonal with diagonal blocks given by the submodels.\n",
    "Joint estimation is not yet implemented.\n",
    "\n",
    "The censored and truncated count models were developed mainly to support the hurdle model. However, the left truncated count models have other applications than supporting the hurdle models. The right censored models are not of separate interest because they only support binary observations that can be modeled by GLM-Binomial, Logit or Probit.\n",
    "\n",
    "For the hurdle model there is a single class `HurdleCountModel`, that includes the distributions of the submodels as option. \n",
    "Classes for truncated models are currently `TruncatedLFPoisson` and `TruncatedLFNegativeBinomialP`, where \"LF\" stands for left truncation at a fixed, observation independent truncation point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e6105a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eed890e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:10.699961Z",
     "iopub.status.busy": "2026-07-30T22:41:10.699566Z",
     "iopub.status.idle": "2026-07-30T22:41:12.077124Z",
     "shell.execute_reply": "2026-07-30T22:41:12.076443Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from statsmodels.discrete.discrete_model import (\n",
    "    Poisson,\n",
    ")\n",
    "from statsmodels.discrete.truncated_model import (\n",
    "    HurdleCountModel,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85da298e",
   "metadata": {},
   "source": [
    "## Simulating a hurdle model\n",
    "\n",
    "We are simulating a Poisson-Poisson hurdle model explicitly because there are not yet any distribution helper functions for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99c1d162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:12.079700Z",
     "iopub.status.busy": "2026-07-30T22:41:12.079380Z",
     "iopub.status.idle": "2026-07-30T22:41:12.091248Z",
     "shell.execute_reply": "2026-07-30T22:41:12.090432Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[102 335 590 770 816 739 573 402 265 176 116  59  35   7  11   4]\n",
      "4898\n",
      "602\n",
      "93\n",
      "11\n",
      "2\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 102, 1448, 1502, 1049,  542,  234,   81,   31,    6,    5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(987456348)\n",
    "# large sample to get strong results\n",
    "nobs = 5000\n",
    "x = np.column_stack((np.ones(nobs), np.linspace(0, 1, nobs)))\n",
    "\n",
    "mu0 = np.exp(0.5 * 2 * x.sum(1))\n",
    "y = np.random.poisson(mu0, size=nobs)\n",
    "print(np.bincount(y))\n",
    "y_ = y\n",
    "indices = np.arange(len(y))\n",
    "mask = mask0 = y > 0\n",
    "for _ in range(10):\n",
    "\n",
    "    print(mask.sum())\n",
    "    indices = mask  # indices[mask]\n",
    "    if not np.any(mask):\n",
    "        break\n",
    "    mu_ = np.exp(0.5 * x[indices].sum(1))\n",
    "    y[indices] = y_ = np.random.poisson(mu_, size=len(mu_))\n",
    "    np.place(y, mask, y_)\n",
    "    mask = np.logical_and(mask0, y == 0)\n",
    "\n",
    "np.bincount(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6435d35",
   "metadata": {},
   "source": [
    "## Estimating misspecified Poisson Model\n",
    "\n",
    "The data that we generated has zero deflation, this is, we observe fewer zeros than what we would expect in a Poisson model.\n",
    "\n",
    "After fitting the model, we can use the plot function in the poisson diagnostic class to compare the expected predictive distribution and the realized frequencies. The shows that the Poisson model overestimates the number of zeros and underestimates counts of one and two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fdd59b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:12.092981Z",
     "iopub.status.busy": "2026-07-30T22:41:12.092804Z",
     "iopub.status.idle": "2026-07-30T22:41:12.112647Z",
     "shell.execute_reply": "2026-07-30T22:41:12.111837Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 1.668079\n",
      "         Iterations 4\n",
      "                          Poisson Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                 5000\n",
      "Model:                        Poisson   Df Residuals:                     4998\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Thu, 30 Jul 2026   Pseudo R-squ.:                0.008678\n",
      "Time:                        22:41:12   Log-Likelihood:                -8340.4\n",
      "converged:                       True   LL-Null:                       -8413.4\n",
      "Covariance Type:            nonrobust   LLR p-value:                 1.279e-33\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.6532      0.019     33.642      0.000       0.615       0.691\n",
      "x1             0.3871      0.032     12.062      0.000       0.324       0.450\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "mod_p = Poisson(y, x)\n",
    "res_p = mod_p.fit()\n",
    "print(res_p.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91a5c9cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:12.114577Z",
     "iopub.status.busy": "2026-07-30T22:41:12.114385Z",
     "iopub.status.idle": "2026-07-30T22:41:13.038936Z",
     "shell.execute_reply": "2026-07-30T22:41:13.037474Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dia_p = res_p.get_diagnostic()\n",
    "dia_p.plot_probs();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a237e21f",
   "metadata": {},
   "source": [
    "## Estimating the Hurdle Model\n",
    "\n",
    "Next, we estimate the correctly specified Poisson-Poisson hurdle model.\n",
    "\n",
    "Signature and options for the HurdleCountModel shows that poisson-poisson is the default, so we do not need to specify any options when creating this model.\n",
    "\n",
    "`HurdleCountModel(endog, exog, offset=None, dist='poisson', zerodist='poisson', \n",
    "                  p=2, pzero=2, exposure=None, missing='none', **kwargs)`\n",
    "                  \n",
    "The results class of the HurdleCountModel has a `get_diagnostic` method. However, only part of the diagnostic methods are currently available. The plot of the predictive distribution shows very high agreement with the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70065ce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.042254Z",
     "iopub.status.busy": "2026-07-30T22:41:13.041833Z",
     "iopub.status.idle": "2026-07-30T22:41:13.157300Z",
     "shell.execute_reply": "2026-07-30T22:41:13.155979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     HurdleCountModel Regression Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                 5000\n",
      "Model:               HurdleCountModel   Df Residuals:                     4996\n",
      "Method:                           MLE   Df Model:                            2\n",
      "Date:                Thu, 30 Jul 2026   Pseudo R-squ.:                 0.01503\n",
      "Time:                        22:41:13   Log-Likelihood:                -8004.9\n",
      "converged:               [True, True]   LL-Null:                       -8127.1\n",
      "Covariance Type:            nonrobust   LLR p-value:                 8.901e-54\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "zm_const       0.9577      0.048     20.063      0.000       0.864       1.051\n",
      "zm_x1          1.0576      0.121      8.737      0.000       0.820       1.295\n",
      "const          0.5009      0.024     20.875      0.000       0.454       0.548\n",
      "x1             0.4577      0.039     11.882      0.000       0.382       0.533\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "mod_h = HurdleCountModel(y, x)\n",
    "res_h = mod_h.fit(disp=False)\n",
    "print(res_h.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c3aadbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.160524Z",
     "iopub.status.busy": "2026-07-30T22:41:13.160259Z",
     "iopub.status.idle": "2026-07-30T22:41:13.683900Z",
     "shell.execute_reply": "2026-07-30T22:41:13.682426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dia_h = res_h.get_diagnostic()\n",
    "dia_h.plot_probs();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0827fde3",
   "metadata": {},
   "source": [
    "We can use the Wald test to test whether the parameters of the zero model are the same as the parameters of the zero-truncated count model. The p-value is very small and correctly rejects that the model is just Poisson. We are using a large sample size, so the power of the test will be large in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c000b4cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.686263Z",
     "iopub.status.busy": "2026-07-30T22:41:13.686018Z",
     "iopub.status.idle": "2026-07-30T22:41:13.695567Z",
     "shell.execute_reply": "2026-07-30T22:41:13.694240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<Wald test (chi2): statistic=470.67320754391915, p-value=6.231772522807044e-103, df_denom=2>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_h.wald_test(\"zm_const = const, zm_x1 = x1\", scalar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100ba8a8",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "The hurdle model can be used for prediction for statistics of the overall model and of the two submodels. The statistics that should be predicted is specified using the `which` keyword.\n",
    "\n",
    "The following is taken from the docstring for predict and lists available the options.\n",
    "\n",
    "        which : str (optional)\n",
    "            Statitistic to predict. Default is 'mean'.\n",
    "\n",
    "            - 'mean' : the conditional expectation of endog E(y | x)\n",
    "            - 'mean-main' : mean parameter of truncated count model.\n",
    "              Note, this is not the mean of the truncated distribution.\n",
    "            - 'linear' : the linear predictor of the truncated count model.\n",
    "            - 'var' : returns the estimated variance of endog implied by the\n",
    "              model.\n",
    "            - 'prob-main' : probability of selecting the main model which is\n",
    "              the probability of observing a nonzero count P(y > 0 | x).\n",
    "            - 'prob-zero' : probability of observing a zero count. P(y=0 | x).\n",
    "              This is equal to is ``1 - prob-main``\n",
    "            - 'prob-trunc' : probability of truncation of the truncated count\n",
    "              model. This is the probability of observing a zero count implied\n",
    "              by the truncation model.\n",
    "            - 'mean-nonzero' : expected value conditional on having observation\n",
    "              larger than zero, E(y | X, y>0)\n",
    "            - 'prob' : probabilities of each count from 0 to max(endog), or\n",
    "              for y_values if those are provided. This is a multivariate\n",
    "              return (2-dim when predicting for several observations).\n",
    "              \n",
    "These options are available in the `predict` and the `get_prediction` methods of the results class.\n",
    "\n",
    "For the following example, we create a set of explanatory variables that are taken from the original data at equal spaced intervals. Then we can predict the available statistics conditional on these explanatory variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9757da68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.697696Z",
     "iopub.status.busy": "2026-07-30T22:41:13.697459Z",
     "iopub.status.idle": "2026-07-30T22:41:13.705701Z",
     "shell.execute_reply": "2026-07-30T22:41:13.704333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        ],\n",
       "       [1.        , 0.20004001],\n",
       "       [1.        , 0.40008002],\n",
       "       [1.        , 0.60012002],\n",
       "       [1.        , 0.80016003]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "which_options = [\n",
    "    \"mean\",\n",
    "    \"mean-main\",\n",
    "    \"linear\",\n",
    "    \"mean-nonzero\",\n",
    "    \"prob-zero\",\n",
    "    \"prob-main\",\n",
    "    \"prob-trunc\",\n",
    "    \"var\",\n",
    "    \"prob\",\n",
    "]\n",
    "ex = x[slice(None, None, nobs // 5), :]\n",
    "ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3614ee25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.708055Z",
     "iopub.status.busy": "2026-07-30T22:41:13.707825Z",
     "iopub.status.idle": "2026-07-30T22:41:13.717678Z",
     "shell.execute_reply": "2026-07-30T22:41:13.716282Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "     [1.89150663 2.07648059 2.25555158 2.43319456 2.61673457]\n",
      "mean-main\n",
      "     [1.65015181 1.8083782  1.98177629 2.17180081 2.38004602]\n",
      "linear\n",
      "     [0.50086729 0.59243042 0.68399356 0.77555669 0.86711982]\n",
      "mean-nonzero\n",
      "     [2.04231955 2.16292424 2.29857565 2.45116551 2.62277411]\n",
      "prob-zero\n",
      "     [0.07384394 0.0399661  0.01871771 0.00733159 0.00230273]\n",
      "prob-main\n",
      "     [0.92615606 0.9600339  0.98128229 0.99266841 0.99769727]\n",
      "prob-trunc\n",
      "     [0.19202076 0.16391977 0.1378242  0.11397219 0.09254632]\n",
      "var\n",
      "     [1.43498239 1.51977118 1.63803729 1.7971727  1.99738345]\n",
      "prob\n",
      "     [[7.38439416e-02 3.63208532e-01 2.99674608e-01 1.64836199e-01\n",
      "  6.80011882e-02 2.24424568e-02 6.17224344e-03 1.45501981e-03\n",
      "  3.00125448e-04 5.50280612e-05]\n",
      " [3.99660987e-02 3.40376213e-01 3.07764462e-01 1.85518182e-01\n",
      "  8.38717591e-02 3.03343722e-02 9.14266959e-03 2.36191491e-03\n",
      "  5.33904431e-04 1.07277904e-04]\n",
      " [1.87177088e-02 3.10869602e-01 3.08037002e-01 2.03486809e-01\n",
      "  1.00816333e-01 3.99590837e-02 1.31983274e-02 3.73659033e-03\n",
      "  9.25635762e-04 2.03822556e-04]\n",
      " [7.33159258e-03 2.77316512e-01 3.01138113e-01 2.18003999e-01\n",
      "  1.18365316e-01 5.14131777e-02 1.86098635e-02 5.77384524e-03\n",
      "  1.56745522e-03 3.78244503e-04]\n",
      " [2.30272798e-03 2.42169151e-01 2.88186862e-01 2.28632665e-01\n",
      "  1.36039066e-01 6.47558475e-02 2.56869828e-02 8.73374304e-03\n",
      "  2.59833880e-03 6.87129546e-04]]\n"
     ]
    }
   ],
   "source": [
    "for w in which_options:\n",
    "    print(w)\n",
    "    pred = res_h.predict(ex, which=w)\n",
    "    print(\"    \", pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbc9ebed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.719705Z",
     "iopub.status.busy": "2026-07-30T22:41:13.719469Z",
     "iopub.status.idle": "2026-07-30T22:41:13.736477Z",
     "shell.execute_reply": "2026-07-30T22:41:13.735090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "     [1.89150663 2.07648059 2.25555158 2.43319456 2.61673457]\n",
      "  se [0.07877461 0.05693768 0.05866892 0.09551274 0.15359057]\n",
      "mean-main\n",
      "     [1.65015181 1.8083782  1.98177629 2.17180081 2.38004602]\n",
      "  se [0.03959242 0.03164634 0.02471869 0.02415162 0.03453261]\n",
      "linear\n",
      "     [0.50086729 0.59243042 0.68399356 0.77555669 0.86711982]\n",
      "  se [0.04773779 0.03148549 0.02960421 0.04397859 0.06453261]\n",
      "mean-nonzero\n",
      "     [2.04231955 2.16292424 2.29857565 2.45116551 2.62277411]\n",
      "  se [0.02978486 0.02443098 0.01958745 0.0196433  0.02881753]\n",
      "prob-zero\n",
      "     [0.07384394 0.0399661  0.01871771 0.00733159 0.00230273]\n",
      "  se [0.00918583 0.00405155 0.00220446 0.00158494 0.00090255]\n",
      "prob-main\n",
      "     [0.92615606 0.9600339  0.98128229 0.99266841 0.99769727]\n",
      "  se [0.00918583 0.00405155 0.00220446 0.00158494 0.00090255]\n",
      "prob-trunc\n",
      "     [0.19202076 0.16391977 0.1378242  0.11397219 0.09254632]\n",
      "  se [0.00760257 0.00518746 0.00340683 0.00275261 0.00319587]\n",
      "var\n",
      "     [1.43498239 1.51977118 1.63803729 1.7971727  1.99738345]\n",
      "  se [0.04853902 0.03615054 0.02747485 0.02655145 0.03733328]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.14.6/x64/lib/python3.14/site-packages/statsmodels/discrete/discrete_model.py:5298: UserWarning: using default log-link in get_prediction\n",
      "  res = pred.get_prediction(\n"
     ]
    }
   ],
   "source": [
    "for w in which_options[:-1]:\n",
    "    print(w)\n",
    "    pred = res_h.get_prediction(ex, which=w)\n",
    "    print(\"    \", pred.predicted)\n",
    "    print(\"  se\", pred.se)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e356eb1b",
   "metadata": {},
   "source": [
    "The option `which=\"prob\"` returns an array of predicted probabilities for each row of the predict `exog`.\n",
    "We are often interested in the mean probabilities averaged over all exog. The prediction methods have an option `average=True` to compute the average of the predicted values across observations and the corresponding standard errors and confidence intervals for those averaged predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d90afc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.738741Z",
     "iopub.status.busy": "2026-07-30T22:41:13.738487Z",
     "iopub.status.idle": "2026-07-30T22:41:13.750707Z",
     "shell.execute_reply": "2026-07-30T22:41:13.747930Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     [2.84324139e-02 3.06788002e-01 3.00960210e-01 2.00095571e-01\n",
      " 1.01418732e-01 4.17809876e-02 1.45620174e-02 4.41222267e-03\n",
      " 1.18509193e-03 2.86300514e-04]\n",
      "  se [2.81472152e-03 5.00830805e-03 1.37524763e-03 1.87343644e-03\n",
      " 1.99068649e-03 1.23878525e-03 5.78099173e-04 2.21180110e-04\n",
      " 7.25021189e-05 2.08872558e-05]\n"
     ]
    }
   ],
   "source": [
    "pred = res_h.get_prediction(ex, which=\"prob\", average=True)\n",
    "print(\"    \", pred.predicted)\n",
    "print(\"  se\", pred.se)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09602e97",
   "metadata": {},
   "source": [
    "We use the panda DataFrame to get a display that is easier to read. The \"predicted\" column shows the probability mass function for the predicted distribution of response values averaged of our 5 grid points of exog. The probabilities do not add up to one because counts larger than those observed have positive probability and are missing in the table, although in this example that probability is small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "77c7eff9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.752867Z",
     "iopub.status.busy": "2026-07-30T22:41:13.752629Z",
     "iopub.status.idle": "2026-07-30T22:41:13.768023Z",
     "shell.execute_reply": "2026-07-30T22:41:13.766711Z"
    }
   },
   "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>predicted</th>\n",
       "      <th>se</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028432</td>\n",
       "      <td>0.002815</td>\n",
       "      <td>0.022916</td>\n",
       "      <td>0.033949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.306788</td>\n",
       "      <td>0.005008</td>\n",
       "      <td>0.296972</td>\n",
       "      <td>0.316604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300960</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.298265</td>\n",
       "      <td>0.303656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200096</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.196424</td>\n",
       "      <td>0.203767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.101419</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>0.097517</td>\n",
       "      <td>0.105320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.041781</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.039353</td>\n",
       "      <td>0.044209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.013429</td>\n",
       "      <td>0.015695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.003979</td>\n",
       "      <td>0.004846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001185</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.001327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted        se  ci_lower  ci_upper\n",
       "0   0.028432  0.002815  0.022916  0.033949\n",
       "1   0.306788  0.005008  0.296972  0.316604\n",
       "2   0.300960  0.001375  0.298265  0.303656\n",
       "3   0.200096  0.001873  0.196424  0.203767\n",
       "4   0.101419  0.001991  0.097517  0.105320\n",
       "5   0.041781  0.001239  0.039353  0.044209\n",
       "6   0.014562  0.000578  0.013429  0.015695\n",
       "7   0.004412  0.000221  0.003979  0.004846\n",
       "8   0.001185  0.000073  0.001043  0.001327\n",
       "9   0.000286  0.000021  0.000245  0.000327"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp_h = pred.summary_frame()\n",
    "dfp_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "185a5042",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.771655Z",
     "iopub.status.busy": "2026-07-30T22:41:13.771394Z",
     "iopub.status.idle": "2026-07-30T22:41:13.778722Z",
     "shell.execute_reply": "2026-07-30T22:41:13.777161Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.9999215487936677), np.float64(7.84512063323195e-05))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_larger9 = pred.predicted.sum()\n",
    "prob_larger9, 1 - prob_larger9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75795cd",
   "metadata": {},
   "source": [
    "`get_prediction` returns in this case an instance of the base `PredictionResultsDelta` class.\n",
    "\n",
    "Inferential statistics like standard errors, p-values and confidence interval for nonlinear functions that depend on several distribution parameters are computed using the delta method. Inference for predictions is based on the normal distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8124e02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.781190Z",
     "iopub.status.busy": "2026-07-30T22:41:13.780950Z",
     "iopub.status.idle": "2026-07-30T22:41:13.788195Z",
     "shell.execute_reply": "2026-07-30T22:41:13.786742Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.base._prediction_inference.PredictionResultsDelta at 0x7fafb4cd6140>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c1d314d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.790342Z",
     "iopub.status.busy": "2026-07-30T22:41:13.790106Z",
     "iopub.status.idle": "2026-07-30T22:41:13.797890Z",
     "shell.execute_reply": "2026-07-30T22:41:13.796376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<scipy.stats._continuous_distns.norm_gen at 0x7fafbbde27b0>, ())"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.dist, pred.dist_args"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b032002",
   "metadata": {},
   "source": [
    "We can compare the distribution predicted by the hurdle model with the one predicted by the Poisson model that we estimated earlier. The last column, \"diff\", shows that Poisson model overestimates the number of zeros by around 8% of observations and underestimates the counts of 1 and 2 by 7%, resp. 3.7% at the average over the `exog` grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd01ba63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.800228Z",
     "iopub.status.busy": "2026-07-30T22:41:13.800002Z",
     "iopub.status.idle": "2026-07-30T22:41:13.817964Z",
     "shell.execute_reply": "2026-07-30T22:41:13.816610Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>se</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "      <th>poisson</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028432</td>\n",
       "      <td>0.002815</td>\n",
       "      <td>0.022916</td>\n",
       "      <td>0.033949</td>\n",
       "      <td>0.107848</td>\n",
       "      <td>0.079416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.306788</td>\n",
       "      <td>0.005008</td>\n",
       "      <td>0.296972</td>\n",
       "      <td>0.316604</td>\n",
       "      <td>0.237020</td>\n",
       "      <td>-0.069768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300960</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.298265</td>\n",
       "      <td>0.303656</td>\n",
       "      <td>0.263523</td>\n",
       "      <td>-0.037437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200096</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.196424</td>\n",
       "      <td>0.203767</td>\n",
       "      <td>0.197657</td>\n",
       "      <td>-0.002439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.101419</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>0.097517</td>\n",
       "      <td>0.105320</td>\n",
       "      <td>0.112511</td>\n",
       "      <td>0.011093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.041781</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.039353</td>\n",
       "      <td>0.044209</td>\n",
       "      <td>0.051833</td>\n",
       "      <td>0.010052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.013429</td>\n",
       "      <td>0.015695</td>\n",
       "      <td>0.020124</td>\n",
       "      <td>0.005561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.003979</td>\n",
       "      <td>0.004846</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.002356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001185</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.000827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted        se  ci_lower  ci_upper   poisson      diff\n",
       "0   0.028432  0.002815  0.022916  0.033949  0.107848  0.079416\n",
       "1   0.306788  0.005008  0.296972  0.316604  0.237020 -0.069768\n",
       "2   0.300960  0.001375  0.298265  0.303656  0.263523 -0.037437\n",
       "3   0.200096  0.001873  0.196424  0.203767  0.197657 -0.002439\n",
       "4   0.101419  0.001991  0.097517  0.105320  0.112511  0.011093\n",
       "5   0.041781  0.001239  0.039353  0.044209  0.051833  0.010052\n",
       "6   0.014562  0.000578  0.013429  0.015695  0.020124  0.005561\n",
       "7   0.004412  0.000221  0.003979  0.004846  0.006769  0.002356\n",
       "8   0.001185  0.000073  0.001043  0.001327  0.002012  0.000827\n",
       "9   0.000286  0.000021  0.000245  0.000327  0.000537  0.000250"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_p = res_p.get_prediction(ex, which=\"prob\", average=True)\n",
    "dfp_p = pred_p.summary_frame()\n",
    "dfp_h[\"poisson\"] = dfp_p[\"predicted\"]\n",
    "dfp_h[\"diff\"] = dfp_h[\"poisson\"] - dfp_h[\"predicted\"]\n",
    "dfp_h"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da7fb38",
   "metadata": {},
   "source": [
    "## Other post-estimation\n",
    "\n",
    "The estimated hurdle model can be use for wald test of parameters and for prediction. Other maximum likelihood statistics such as loglikelihood value and information criteria are also available. \n",
    "\n",
    "However, some post-estimation methods that require helper functions that are not needed for estimation, parameter inference and prediction are not yet available. The main methods that are not supported yet are `score_test`, `get_distribution`, and `get_influence`. Diagnostic measures in `get_diagnostics` are only available for statistics that are based on prediction.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e821f6e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.820478Z",
     "iopub.status.busy": "2026-07-30T22:41:13.820241Z",
     "iopub.status.idle": "2026-07-30T22:41:13.827392Z",
     "shell.execute_reply": "2026-07-30T22:41:13.826076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(-8004.904002793644),\n",
       " 4996,\n",
       " np.float64(16017.808005587289),\n",
       " np.float64(16043.876778352953))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_h.llf, res_h.df_resid, res_h.aic, res_h.bic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d2ae62",
   "metadata": {},
   "source": [
    "Is there excess dispersion? We can use the pearson residuals to compute a pearson chi2 statistics which should be close to 1 if the model is correctly specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f1a5124",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.829495Z",
     "iopub.status.busy": "2026-07-30T22:41:13.829261Z",
     "iopub.status.idle": "2026-07-30T22:41:13.837556Z",
     "shell.execute_reply": "2026-07-30T22:41:13.836223Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9989670114949286)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(res_h.resid_pearson**2).sum() / res_h.df_resid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff9df60",
   "metadata": {},
   "source": [
    "The diagnostic class also has the predictive distribution which is used in the diagnostic plots. No other statistics or tests are currently availalbe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "50ba5545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.839962Z",
     "iopub.status.busy": "2026-07-30T22:41:13.839730Z",
     "iopub.status.idle": "2026-07-30T22:41:13.847275Z",
     "shell.execute_reply": "2026-07-30T22:41:13.845926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02044612, 0.29147174, 0.29856288, 0.20740118, 0.10990976,\n",
       "       0.04737579, 0.0172898 , 0.00548983, 0.00154646, 0.00039214])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia_h.probs_predicted.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c913a1bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-30T22:41:13.849373Z",
     "iopub.status.busy": "2026-07-30T22:41:13.849150Z",
     "iopub.status.idle": "2026-07-30T22:41:13.856902Z",
     "shell.execute_reply": "2026-07-30T22:41:13.855575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.10849337,  1.10830496, -0.89188344, -0.89207183,  1.10773978,\n",
       "       -0.8924486 , -0.89263697,  0.10717466,  0.1069863 ,  0.10679794])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_h.resid[:10]"
   ]
  }
 ],
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