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@kenjisato
Created June 15, 2015 05:11
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2015 経済動学 講義資料 (2015-06-15)
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{
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import itertools\n",
"\n",
"import numpy as np\n",
"import scipy.linalg as la\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class LinearSystem:\n",
" \"\"\"Linear/Affine system\"\"\"\n",
"\n",
" def __init__(self, A, origin=None):\n",
" self.A = A\n",
" self.origin = np.zeros(self.dim) if origin is None else np.asarray(origin)\n",
"\n",
" def forward(self, x):\n",
" return np.dot(self.A, np.asarray(x) - self.origin) + self.origin\n",
"\n",
" def reverse(self, x):\n",
" return la.solve(self.A, np.asarray(x) - self.origin) + self.origin\n",
"\n",
" def eig(self):\n",
" \"\"\"Eigen values and eigen vectors\"\"\"\n",
" return la.eig(self.A)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Ramsey:\n",
" \"\"\"One-sector Ramsey model\"\"\"\n",
"\n",
" def __init__(self, A, α, ρ):\n",
" self.A, self.α, self.ρ = A, α, ρ\n",
"\n",
" def f(self, x):\n",
" \"\"\"Production function\"\"\"\n",
" return self.A * x ** self.α\n",
"\n",
" def U(self, x):\n",
" \"\"\"Utility from consumption\"\"\"\n",
" return np.log(x)\n",
"\n",
" def u(self, x, y):\n",
" \"\"\"Reduced form utility\"\"\"\n",
" return self.U(self.f(x) - y)\n",
"\n",
" def is_feasible(self, x, y):\n",
" \"\"\"Checks feasibility\"\"\"\n",
" return x >= 0 and y >= 0 and self.f(x) >= y\n",
"\n",
" def steady_state(self):\n",
" \"\"\"Steady state for the model\"\"\"\n",
" A, α, ρ = self.A, self.α, self.ρ\n",
" koo = (ρ * α * A) ** (1 / (1 - α))\n",
" return np.array([koo, koo])\n",
"\n",
" def forward(self, x):\n",
" \"\"\"Forward evolution\"\"\"\n",
" A, α, ρ = self.A, self.α, self.ρ\n",
" return np.array([\n",
" x[1],\n",
" ((1 + ρ * α) * A * x[1] ** α -\n",
" ρ * α * (A ** 2) * (x[1] ** (α - 1)) * (x[0] ** α))\n",
" ])\n",
"\n",
" def reverse(self, x):\n",
" \"\"\"Backward evolution\"\"\"\n",
" A, α, ρ = self.A, self.α, self.ρ\n",
" return np.array([\n",
" ((((1 + ρ * α) * A * x[0] ** α - x[1]) /\n",
" (ρ * α * A ** 2 * x[0] ** (α - 1))) ** (1 / α)),\n",
" x[0]\n",
" ])\n",
"\n",
" def jacobian(self, x=None):\n",
" \"\"\"Returns jacobian matrix\"\"\"\n",
" A, α, ρ = self.A, self.α, self.ρ\n",
"\n",
" def _jacobian(x):\n",
" \"\"\"Jacobian as a function of state\"\"\"\n",
" return np.array([\n",
" [0, 1],\n",
" [-ρ * ((α * A) ** 2) * ((x[0] * x[1]) ** (α - 1)),\n",
" (α * (1 + ρ * α) * A * x[0] ** (α - 1) -\n",
" ρ * α * (α-1) * (A ** 2) * (x[0]**α) * (x[1] ** (α-2)))]\n",
" ])\n",
"\n",
" if x is None:\n",
" return _jacobian(self.steady_state())\n",
" return _jacobian(x)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def linearize(diffsys, ss):\n",
" \"\"\"Linearize differentiable system around a steady state\"\"\"\n",
" if not np.allclose(diffsys.forward(ss), ss):\n",
" raise ValueError(\"Not a steady state\")\n",
" return LinearSystem(diffsys.jacobian(ss), ss)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Simulation:\n",
" \"\"\"Simulation of a dynamical system\"\"\"\n",
"\n",
" def __init__(self, system, x0=None, duration=np.Inf, inverse=False, domain=None):\n",
" self.system = system\n",
" self.x0 = x0\n",
" self.duration = duration\n",
" self.inverse = inverse\n",
" if domain is not None:\n",
" self.domain = domain\n",
" else:\n",
" self.domain = lambda x: True\n",
"\n",
" def __iter__(self):\n",
" x = self.x0[:]\n",
" tick = self.system.forward if not self.inverse else self.system.reverse\n",
" t = itertools.count()\n",
" while next(t) < self.duration and self.domain(x):\n",
" yield x\n",
" x = tick(x)\n",
" del t\n",
"\n",
" def reset(self, x0=None, duration=None, inverse=None):\n",
" \"\"\"Reset simulation parameters\"\"\"\n",
"\n",
" if x0 is not None:\n",
" self.x0 = x0[:]\n",
" if duration is not None:\n",
" self.duration = duration\n",
" if inverse is not None:\n",
" self.inverse = inverse\n",
" return self\n",
"\n",
" def __repr__(self):\n",
" text = \"Simulation({},\\n\"\n",
" text += \" x0={},\\n\"\n",
" text += \" duration={})\"\n",
" return text.format(\n",
" str(self.system),\n",
" str(self.x0),\n",
" str(self.duration)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def find_initial_near_eqm(linsys, eps=1e-5):\n",
" init_vals = []\n",
" signs = [-1, 1]\n",
" lambdas, V = linsys.eig()\n",
" for i, lambda_ in enumerate(lambdas):\n",
" for sign in signs:\n",
" init_vals.append((lambda_, linsys.origin + sign * eps * V[:, i]))\n",
" return init_vals"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def quiver_plot(ax, path, **kwargs):\n",
" options = {\n",
" 'scale_units': 'xy',\n",
" 'angles': 'xy',\n",
" 'scale': 1,\n",
" 'width': 0.003,\n",
" 'color': 'black'\n",
" }\n",
" options.update(kwargs)\n",
" path = np.asarray(path)\n",
" return ax.quiver(path[:-1, 0], path[:-1, 1],\n",
" path[1:, 0]-path[:-1, 0], path[1:, 1]-path[:-1, 1], **options)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1069cdda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ramsey = Ramsey(A=1.2, α=0.4, ρ=0.98)\n",
"linearized = linearize(ramsey, ramsey.steady_state())\n",
"\n",
"sim_ramsey = Simulation(ramsey, domain=lambda x: ramsey.is_feasible(*x))\n",
"sim_linear = Simulation(linearized)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"kmax = 1.8 * ramsey.steady_state()[0]\n",
"k = np.linspace(0.0, kmax, 200)\n",
"ax.plot(k, ramsey.f(k), color='black')\n",
"ax.fill_between(k, ramsey.f(k), 0.0, color='black', alpha=0.1)\n",
"ax.plot(k, k, color='black', linestyle='dashed')\n",
"\n",
"init_vals = find_initial_near_eqm(linearized, eps=1e-6)\n",
"for lambda_, inits in init_vals:\n",
" if abs(lambda_) < 1:\n",
" path = list(sim_linear.reset(inits, 15, inverse=True))\n",
" path.reverse()\n",
" else:\n",
" path = list(sim_linear.reset(inits, 20, inverse=False))\n",
" quiver_plot(ax, path, width=0.004, color='black')\n",
"\n",
"for lambda_, inits in init_vals:\n",
" if abs(lambda_) < 1:\n",
" path = list(sim_ramsey.reset(inits, 16, inverse=True))\n",
" path.reverse()\n",
" else:\n",
" path = list(sim_ramsey.reset(inits, 16, inverse=False))\n",
" quiver_plot(ax, path, width=0.004, color='red')\n",
"\n",
"ax.set_xlim([0, kmax])\n",
"ax.set_ylim([0, ramsey.f(k[-1])])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_ramsey(A, α, ρ):\n",
" ramsey = Ramsey(A, α, ρ)\n",
" linearized = linearize(ramsey, ramsey.steady_state())\n",
"\n",
" sim_ramsey = Simulation(ramsey, domain=lambda x: ramsey.is_feasible(*x))\n",
" sim_linear = Simulation(linearized)\n",
"\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
"\n",
" kmax = 1.8 * ramsey.steady_state()[0]\n",
" k = np.linspace(0.0, kmax, 200)\n",
" ax.plot(k, ramsey.f(k), color='black')\n",
" ax.fill_between(k, ramsey.f(k), 0.0, color='black', alpha=0.1)\n",
" ax.plot(k, k, color='black', linestyle='dashed')\n",
"\n",
" init_vals = find_initial_near_eqm(linearized, eps=1e-6)\n",
" for lambda_, inits in init_vals:\n",
" if abs(lambda_) < 1:\n",
" path = list(sim_linear.reset(inits, 15, inverse=True))\n",
" path.reverse()\n",
" else:\n",
" path = list(sim_linear.reset(inits, 20, inverse=False))\n",
" quiver_plot(ax, path, width=0.004, color='black')\n",
"\n",
" for lambda_, inits in init_vals:\n",
" if abs(lambda_) < 1:\n",
" path = list(sim_ramsey.reset(inits, 16, inverse=True))\n",
" path.reverse()\n",
" else:\n",
" path = list(sim_ramsey.reset(inits, 16, inverse=False))\n",
" quiver_plot(ax, path, width=0.004, color='red')\n",
"\n",
" ax.set_xlim([0, kmax])\n",
" ax.set_ylim([0, ramsey.f(k[-1])])\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10752b080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ramsey(1.1, 0.35, 0.9)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"YII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x10752b978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ramsey(1.01, 0.35, 0.8)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x106a419e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_ramsey(1.2, 0.35, 0.99)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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