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   "source": [
    "# Weekly exercise 14: Stochastic consumption-savings model in finite horizon\n",
    "\n",
    "In this exercise you continue building up the `deaton()` class by adding the finite horizon version of the model.\n",
    "\n",
    "Start by copying the `deaton()` class code from lecture notebook 36.  Make the following modifications:\n",
    "\n",
    "- add an attribute horizon to the model which is set to ‘infinite’ by default, but can take numerical values fixing the number of periods in the model $ T $  \n",
    "- modify the `__repr__()` method to output whether the model is finite or infinite horizon  \n",
    "- code up the backwards induction solver for the finite horizon version of the model in a separate method `solver_backwards_induction()` with the only argument callback function (but using horizon attribute for the maximum number of periods).  See lecture notebook 27 for examples of backward induction solvers  \n",
    "- modify the simulator method to default to the $ T $ set in the horizon attribute when it is not equal ‘infinite’  \n",
    "\n",
    "\n",
    "Run separate simulations of the model for a few values of parameters, and discuss their similarities and differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide-output": false
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide-output": false
   },
   "outputs": [],
   "source": [
    "m = deaton(ngrid=100,nchgrid=250,sigma=.2,R=1.05,beta=.975,nquad=10)\n",
    "m.horizon=12\n",
    "print(m)\n",
    "v,c = m.solve_plot(solver='vfi')\n",
    "sims = m.simulator(init_wealth=[1,2,3],T=5)"
   ]
  }
 ],
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  "title": "Weekly exercise 14: Stochastic consumption-savings model in finite horizon"
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