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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imports:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions and simulation:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 1000\n",
"θ = 0.8\n",
"\n",
"d = np.sqrt(1-theta**2)\n",
"\n",
"def p(x,y):\n",
" \"Stochastic kernel for the TAR model\"\n",
" return norm().pdf((y - θ * np.abs(x))/d)/d\n",
"\n",
"Z = norm().rvs(n)\n",
"X = np.empty(n)\n",
"\n",
"for t in range(n-1):\n",
" X[t+1] = θ * np.abs(X[t]) + d * Z[t+1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up grid, compute kernel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid_size = 200\n",
"ys = np.linspace(-3, 3, grid_size)\n",
"kernel = np.empty(k)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"for (i, y) in enumerate(ys):\n",
" kernel[i] = np.mean(p(X, y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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y5rGVldIeVuwihRCsACSM96cuXAfOJjfXpgMldgcitRCsACTMzp12XEmXLlJh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\nECf/H1sD6/hHiLwdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f042d9635f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(10, 7))\n",
"ax.plot(ys, kernel, 'b-', lw=2, alpha=0.6, label='look ahead estimate')\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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