{
"cells": [
{
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"metadata": {
"slideshow": {
"slide_type": "slide"
}
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"source": [
"# Foundations of Computational Economics #35\n",
"\n",
"by Fedor Iskhakov, ANU\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"## Stochastic consumption-savings model with discretized choice\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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},
"source": [
"\n",
"\n",
"[https://youtu.be/3FI_05ok66k](https://youtu.be/3FI_05ok66k)\n",
"\n",
"Description: Deaton model of consumption and savings with random returns. Using quadrature to compute the expectation in the Bellman equation."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Recap: components of the dynamic model\n",
"\n",
"1. **State variables** — vector of variables that describe all relevant\n",
" information about the modeled decision process \n",
"1. **Decision variables** — vector of variables describing the choices, along\n",
" with restrictions on the choice set \n",
"1. **Instantaneous payoff** — utility function, with\n",
" time separable discounted lifetime (overall) utility \n",
"1. **Motion rules** — agent’s beliefs of how state variable evolve\n",
" through time, conditional on choices \n",
"\n",
"\n",
"- **Value function** — maximum attainable lifetime utility \n",
"- **Policy function** — mapping from state space to action space that\n",
" results in maximum lifetime utility (the optimal choice) "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Bellman equation/operator\n",
"\n",
"- Bellman equation has all relevant model parts: fully specifies the model \n",
"- *Equation* when thought of as point-wise equality of two functions \n",
"\n",
"\n",
"$$\n",
"V(M)=\\max_{0 \\le c \\le M}\\big\\{u(c)+\\beta V(\\underset{=M-c}{\\underbrace{M'}})\\big\\}\n",
"$$\n",
"\n",
"- *Operator* when thought of as map that takes one (value) function $ V(\\cdot) $ as input\n",
" (it goes to the RHS), and returns another (value) function \n",
"\n",
"\n",
"$$\n",
"T(V)(M)=\\max_{0 \\le c \\le M}\\big\\{u(c)+\\beta V(\\underset{=M-c}{\\underbrace{M'}})\\big\\}\n",
"$$\n",
"\n",
"*Which problem is this?*"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### From cake eating to consumption-savings (in infinite horizon)\n",
"\n",
"$$\n",
"V(M)=\\max_{0 \\le c \\le M}\\big\\{u(c)+\\beta V\\big(\\underset{=M'}{\\underbrace{R(M-c)+y}}\\big)\\big\\}\n",
"$$\n",
"\n",
"What has changed?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Consumption-savings problem (Deaton model)\n",
"\n",
"New interpretation:\n",
"\n",
"- Wealth in the beginning of the period $ t $ is $ M $ \n",
"- Consumption during the period $ t $ is $ 0 \\le c \\le M $ \n",
"- No borrowing is allowed \n",
"- Discount factor $ \\beta $, time separable utility $ u(c) = \\log(c) $ \n",
"- Gross return on savings $ R $, *can be stochastic* \n",
"- Constant income $ y \\ge 0 $, *can be stochastic* \n",
"\n",
"\n",
"For cake eating problem we have $ R=1 $ and $ y=0 $."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Stochastic consumption-savings problem\n",
"\n",
"$$\n",
"V(M)=\\max_{0 \\le c \\le M}\\big\\{u(c)+\\beta \\mathbb{E}_{R,y} V\\big(\\underset{=M'}{\\underbrace{\\tilde{R}(M-c)+\\tilde{y}}}\\big)\\big\\}\n",
"$$\n",
"\n",
"- we focus on income fluctuations $ \\tilde{y} $ and fix $ \\tilde{R} $ \n",
"- let stochastic income $ \\tilde{y} $ follow a log-normal distribution with parameters $ \\mu = 0 $ and $ \\sigma $ to be specified \n",
"- then $ \\tilde{y} > 0 $ and $ \\mathbb{E}(\\tilde{y}) = \\exp(\\sigma^2/2) $ \n",
"- for backward compatibility add $ y=0 $ special case \n",
"\n",
"\n",
"[https://en.wikipedia.org/wiki/Log-normal_distribution](https://en.wikipedia.org/wiki/Log-normal_distribution)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Solving stochastic consumption-savings model\n",
"\n",
"VFI solution method will still work\n",
"\n",
"1. Discretize state space and set the initial values for $ V_0(M) $ at state grid \n",
"1. Evaluate Bellman operator to compute $ V_i(M) $ from $ V_{i-1}(M) $, and simultaneously compute the optimal decisions (policy) $ c_i(M) $ at state grid \n",
"1. Check for convergence in value function space, repeat the last step if not yet converged \n",
"\n",
"\n",
"**But now need to compute the expectation when calculating the maximand in the Bellman equation**\n",
"\n",
"Quadrature!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Gauss-Legendre Quadrature\n",
"\n",
"- Domain $ [-1,1] $ \n",
"- Weighting $ 1 $ \n",
"\n",
"\n",
"$$\n",
"\\int_{-1}^1 f(x)dx = \\sum_{i=1}^{n} w_i f(x_i) + \\frac{2^{2n+1}(n!)^4}{(2n+1)!(2n)!} \\frac{f^{(2n)}(\\xi)}{(2n)!}\n",
"$$\n",
"\n",
"Nodes and weights are returned by `numpy.polynomial.legendre.leggauss()`\n",
"\n",
"[https://numpy.org/doc/stable/reference/generated/numpy.polynomial.legendre.leggauss.html](https://numpy.org/doc/stable/reference/generated/numpy.polynomial.legendre.leggauss.html)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Details on the quadrature calculation\n",
"\n",
"$$\n",
"\\mathbb{E}_{y} V\\big(M'(\\tilde{y})\\big) = \\int_{-\\infty}^{\\infty} V\\big(M'(y)\\big) f(y) dy =\n",
"$$\n",
"\n",
"- Change of variable $ z = F(y) $, so $ dz = F'(y) dy = f(y) dy $, where $ F(y) $ and $ f(y) $ are cdf and pdf \n",
"\n",
"\n",
"$$\n",
"= \\int_0^1 V\\big(M'\\big(F^{-1}(z)\\big)\\big) dz =\n",
"$$\n",
"\n",
"- Change of variable $ x = 2z-1 $, so that $ dx = 2 dz $ \n",
"\n",
"\n",
"$$\n",
"= \\int_{-1}^1 V\\big(M'\\big(F^{-1}\\left(\\frac{x+1}{2}\\right)\\big)\\big) \\frac{dx}{2} \\approx \\sum_i \\frac{w_i}{2} V\\big(M'\\big(F^{-1}\\left(\\frac{q_i+1}{2}\\right)\\big)\\big)\n",
"$$\n",
"\n",
"- For convenience, convert the the Gauss-Legendre quadrature notes and weights $ \\{q_i,w_i\\} $ to $ q'_i = F^{-1}(\\frac{q_i+1}{2}) $ and $ w'_i = \\frac{w_i}{2} $ "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Adding expectation to cake_discretized() class\n",
"\n",
"Recall the design of Bellman equation code:\n",
"\n",
"- build up the Bellman **maximand as a matrix** with different **states in columns, choices in rows** \n",
"- mask off the infeasible choices with $ -\\infty $ \n",
"- take maximum along `axis=0`, that is among rows in each column \n",
"- resulting vector represents the updated value function for every point of the state space, and is the return of the Bellman operator "
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"Now before adding current utility and taking the maximum, need to compute quadrature:\n",
"\n",
"- build up the next period value function as **3-dim array** with **choices in axis=0, states in axis=1, and quadrature points in axis=2** \n",
"- compute quadrature using `numpy.dot()` n-dim array multiplication to get a matrix \n",
"- add current utility to complete the Bellman maximand as a matrix with states in columns, choices in rows \n",
"- … \n",
"\n",
"\n",
"[https://numpy.org/doc/stable/reference/generated/numpy.dot.html](https://numpy.org/doc/stable/reference/generated/numpy.dot.html)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"hide-output": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function dot in module numpy:\n",
"\n",
"dot(...)\n",
" dot(a, b, out=None)\n",
" \n",
" Dot product of two arrays. Specifically,\n",
" \n",
" - If both `a` and `b` are 1-D arrays, it is inner product of vectors\n",
" (without complex conjugation).\n",
" \n",
" - If both `a` and `b` are 2-D arrays, it is matrix multiplication,\n",
" but using :func:`matmul` or ``a @ b`` is preferred.\n",
" \n",
" - If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`\n",
" and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.\n",
" \n",
" - If `a` is an N-D array and `b` is a 1-D array, it is a sum product over\n",
" the last axis of `a` and `b`.\n",
" \n",
" - If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a\n",
" sum product over the last axis of `a` and the second-to-last axis of `b`::\n",
" \n",
" dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\n",
" \n",
" Parameters\n",
" ----------\n",
" a : array_like\n",
" First argument.\n",
" b : array_like\n",
" Second argument.\n",
" out : ndarray, optional\n",
" Output argument. This must have the exact kind that would be returned\n",
" if it was not used. In particular, it must have the right type, must be\n",
" C-contiguous, and its dtype must be the dtype that would be returned\n",
" for `dot(a,b)`. This is a performance feature. Therefore, if these\n",
" conditions are not met, an exception is raised, instead of attempting\n",
" to be flexible.\n",
" \n",
" Returns\n",
" -------\n",
" output : ndarray\n",
" Returns the dot product of `a` and `b`. If `a` and `b` are both\n",
" scalars or both 1-D arrays then a scalar is returned; otherwise\n",
" an array is returned.\n",
" If `out` is given, then it is returned.\n",
" \n",
" Raises\n",
" ------\n",
" ValueError\n",
" If the last dimension of `a` is not the same size as\n",
" the second-to-last dimension of `b`.\n",
" \n",
" See Also\n",
" --------\n",
" vdot : Complex-conjugating dot product.\n",
" tensordot : Sum products over arbitrary axes.\n",
" einsum : Einstein summation convention.\n",
" matmul : '@' operator as method with out parameter.\n",
" \n",
" Examples\n",
" --------\n",
" >>> np.dot(3, 4)\n",
" 12\n",
" \n",
" Neither argument is complex-conjugated:\n",
" \n",
" >>> np.dot([2j, 3j], [2j, 3j])\n",
" (-13+0j)\n",
" \n",
" For 2-D arrays it is the matrix product:\n",
" \n",
" >>> a = [[1, 0], [0, 1]]\n",
" >>> b = [[4, 1], [2, 2]]\n",
" >>> np.dot(a, b)\n",
" array([[4, 1],\n",
" [2, 2]])\n",
" \n",
" >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\n",
" >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\n",
" >>> np.dot(a, b)[2,3,2,1,2,2]\n",
" 499128\n",
" >>> sum(a[2,3,2,:] * b[1,2,:,2])\n",
" 499128\n",
"\n",
"[[[0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]]\n",
"\n",
" [[0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]]\n",
"\n",
" [[0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]\n",
" [0. 1.]]]\n",
"\n",
" [0.75 0.25] \n",
"\n",
"[[0.25 0.25 0.25 0.25]\n",
" [0.25 0.25 0.25 0.25]\n",
" [0.25 0.25 0.25 0.25]]\n"
]
}
],
"source": [
"# Multiplication of tree-dimensional array by a vector\n",
"import numpy as np\n",
"help(np.dot)\n",
"a = np.zeros((3,4,2)) # 3-dim array 3 by 3 by 2 = two 3x3 matrixes stacked\n",
"a[:,:,1] = np.ones((3,4)) # second of two 3x3 matrixes is all ones\n",
"print(a)\n",
"b = np.asarray([.75,.25])\n",
"print('\\n',b,'\\n')\n",
"print(np.dot(a,b))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"hide-output": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 \n",
"Shape = () \n",
"Ndim = 0\n",
"[[[1]]] \n",
"Shape = (1, 1, 1) \n",
"Ndim = 3\n"
]
}
],
"source": [
"# One more tough spot: adding dimensions up to 3\n",
"a = np.asarray(1)\n",
"# a = np.asarray([1,2,3])\n",
"# a = np.asarray([[1,2,3],[3,4,5]])\n",
"# a = np.asarray([[[1,2,3],[3,4,5]],[[0,1,3],[2,5,6]]])\n",
"print(a,'\\nShape =',a.shape,'\\nNdim =',a.ndim)\n",
"# For tuples: multiplication = repeating given number of times\n",
"# addition = concatenation\n",
"a.shape += (1,)*(3-a.ndim) # add singular dimensions up to 3\n",
"print(a,'\\nShape =',a.shape,'\\nNdim =',a.ndim)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Code for the discretized cake-eating problem from video 32 for comparison"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"hide-output": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = [12, 8]\n",
"\n",
"from scipy import interpolate # Interpolation routines\n",
"\n",
"class cake_discretized():\n",
" '''Class to implement the cake eating model with discretized choice'''\n",
"\n",
" def __init__(self,beta=.9, Wbar=10, ngrid=50, nchgrid=100, optim_ch=True):\n",
" '''Initializer'''\n",
" self.beta = beta # Discount factor\n",
" self.Wbar = Wbar # Upper bound on cake size\n",
" self.ngrid = ngrid # Number of grid points\n",
" self.nchgrid = nchgrid # Number of grid points for choice grid\n",
" self.epsilon = np.finfo(float).eps # smallest positive float number\n",
" self.grid = np.linspace(self.epsilon,Wbar,ngrid) # grid for state space\n",
" self.chgrid = np.linspace(self.epsilon,Wbar,nchgrid) # grid for decision space\n",
" self.optim_ch = optim_ch\n",
"\n",
" def bellman(self,V0):\n",
" '''Bellman operator, V0 is one-dim vector of values on state grid'''\n",
" c = self.chgrid[:,np.newaxis] # column vector\n",
" if self.optim_ch:\n",
" c = c + np.zeros(self.ngrid) # matrix of consumption values\n",
" c *= self.grid/self.Wbar # scale choices to ensure cW] = -np.inf # infeasible choices\n",
" V1 = np.amax(matV1,axis=0,keepdims=False) # maximum in every column\n",
" if self.optim_ch:\n",
" c1 = c[np.argmax(matV1,axis=0),np.arange(self.ngrid)]\n",
" else:\n",
" c1 = c[np.argmax(matV1,axis=0)] # consumption (index of maximum in every column)\n",
" return V1, c1\n",
"\n",
" def solve(self, maxiter=1000, tol=1e-4, callback=None):\n",
" '''Solves the model using VFI (successive approximations)'''\n",
" V0=np.log(self.grid) # on first iteration assume consuming everything\n",
" for iter in range(maxiter):\n",
" V1,c1=self.bellman(V0)\n",
" if callback: callback(iter,self.grid,V1,c1) # callback for making plots\n",
" if np.all(abs(V1-V0) < tol):\n",
" break\n",
" V0=V1\n",
" else: # when i went up to maxiter\n",
" print('No convergence: maximum number of iterations achieved!')\n",
" return V1,c1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"hide-output": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import interpolate\n",
"from scipy.stats import lognorm\n",
"\n",
"class deaton():\n",
" '''Implementation of the stochastic Deaton consumption-savings problem with random income.'''\n",
"\n",
" def __init__(self, Mbar=10,\n",
" ngrid=50, nchgrid=100, nquad=10, interpolation='linear',\n",
" beta=.9, R=1.05, sigma=1.):\n",
" '''Object creator for the stochastic consumption-savings model'''\n",
" self.beta = beta # Discount factor\n",
" self.R = R # Gross interest\n",
" self.sigma = sigma # Param in log-normal income distribution\n",
" self.Mbar = Mbar # Upper bound on wealth\n",
" self.ngrid = ngrid # Number of grid points in the state space\n",
" self.nchgrid = nchgrid # Number of grid points in the decision space\n",
" self.nquad = nquad # Number of quadrature points\n",
" self.interpolation = interpolation # type of interpolation, see below\n",
" # state and choice space grids, as well as quadrature points and weights are set with setter functions below\n",
"\n",
" def __repr__(self):\n",
" '''String representation for the model'''\n",
" return 'Deaton model with beta={:1.3f}, sigma={:1.3f}, gross return={:1.3f}\\nGrids: state {} points up to {:1.1f}, choice {} points, quadrature {} points\\nInterpolation: {}'\\\n",
" .format(self.beta,self.sigma,self.R,self.ngrid,self.Mbar,self.nchgrid,self.nquad,self.interpolation)\n",
"\n",
" @property\n",
" def ngrid(self):\n",
" '''Property getter for the ngrid parameter'''\n",
" return self.__ngrid\n",
"\n",
" @ngrid.setter\n",
" def ngrid(self,ngrid):\n",
" '''Property setter for the ngrid parameter'''\n",
" self.__ngrid = ngrid\n",
" epsilon = np.finfo(float).eps # smallest positive float number difference\n",
" self.grid = np.linspace(epsilon,self.Mbar,ngrid) # grid for state space\n",
"\n",
" @property\n",
" def nchgrid(self):\n",
" '''Property getter for the nchgrid parameter'''\n",
" return self.__nchgrid\n",
"\n",
" @nchgrid.setter\n",
" def nchgrid(self,nchgrid):\n",
" '''Property setter for the nchgrid parameter'''\n",
" self.__nchgrid = nchgrid\n",
" epsilon = np.finfo(float).eps # smallest positive float number difference\n",
" self.chgrid = np.linspace(epsilon,self.Mbar,nchgrid) # grid for state space\n",
"\n",
" @property\n",
" def sigma(self):\n",
" '''Property getter for the sigma parameter'''\n",
" return self.__sigma\n",
"\n",
" @sigma.setter\n",
" def sigma(self,sigma):\n",
" '''Property setter for the sigma parameter'''\n",
" self.__sigma = sigma\n",
" self.__quadrature_setup() # update quadrature points and weights\n",
"\n",
" @property\n",
" def nquad(self):\n",
" '''Property getter for the number of quadrature points'''\n",
" return self.__nquad\n",
"\n",
" @nquad.setter\n",
" def nquad(self,nquad):\n",
" '''Property setter for the number of quadrature points'''\n",
" self.__nquad = nquad\n",
" self.__quadrature_setup() # update quadrature points and weights\n",
"\n",
" def __quadrature_setup(self):\n",
" '''Internal function to set up quadrature points and weights,\n",
" depends on sigma and nquad, therefore called from the property setters\n",
" '''\n",
" try:\n",
" # quadrature points and weights for log-normal distribution\n",
" self.quadp,self.quadw = np.polynomial.legendre.leggauss(self.__nquad) # Gauss-Legendre for [-1,1]\n",
" self.quadp = (self.quadp+1)/2 # rescale to [0,1]\n",
" self.quadp = lognorm.ppf(self.quadp,self.__sigma) # inverse cdf\n",
" self.quadw /= 2 # rescale weights as well\n",
" self.quadp.shape = (1,1,self.__nquad) # quadrature points in third dimension of 3-dim array\n",
" except(AttributeError):\n",
" # when __nquad or __sigma are not yet set\n",
" pass\n",
"\n",
" def utility(self,c):\n",
" '''Utility function'''\n",
" return np.log(c)\n",
"\n",
" def next_period_wealth(self,M,c):\n",
" '''Next period budget, returned for all quadrature points'''\n",
" M1 = self.R*(M-c) # next period wealth without income\n",
" M1.shape += (1,)*(3-M1.ndim) # add singular dimensions up to 3\n",
" # interpolating over income ==> replace with quadrature points\n",
" if self.nquad>1:\n",
" return M1 + self.quadp # 3-dim array\n",
" else:\n",
" return M1 # special case of no income\n",
"\n",
" def interp_func(self,x,f):\n",
" '''Returns the interpolation function for given data'''\n",
" if self.interpolation=='linear':\n",
" return interpolate.interp1d(x,f,kind='slinear',fill_value=\"extrapolate\")\n",
" elif self.interpolation=='quadratic':\n",
" return interpolate.interp1d(x,f,kind='quadratic',fill_value=\"extrapolate\")\n",
" elif self.interpolation=='cubic':\n",
" return interpolate.interp1d(x,f,kind='cubic',fill_value=\"extrapolate\")\n",
" elif self.interpolation=='polynomial':\n",
" p = np.polynomial.polynomial.polyfit(x,f,self.ngrid_state-1)\n",
" return lambda x: np.polynomial.polynomial.polyval(x,p)\n",
" else:\n",
" print('Unknown interpolation type')\n",
" return None\n",
"\n",
" def bellman_discretized(self,V0):\n",
" '''Bellman operator with discretized choice,\n",
" V0 is 1-dim vector of values on the state grid\n",
" '''\n",
" states = self.grid[np.newaxis,:] # state grid as row vector\n",
" choices = self.chgrid[:,np.newaxis] # choice grid as column vector\n",
" M = np.repeat(states,self.nchgrid,axis=0) # current wealth, state grid in columns (repeated rows)\n",
" c = np.repeat(choices,self.ngrid,axis=1) # choice grid in rows (repeated columns)\n",
" c *= self.grid/self.Mbar # scale values of choices to ensure c<=M\n",
" M1 = self.next_period_wealth(M,c) # 3-dim array with quad point in last dimension\n",
" inter = self.interp_func(self.grid,V0) # interpolating function for next period value function\n",
" V1 = inter(M1) # value function at next period wealth, 3-dim array\n",
" EV = np.dot(V1,self.quadw) # expected value function, 2-dim matrix\n",
" MX = self.utility(c) + self.beta*EV # maximand of Bellman equation, 2-dim matrix\n",
" MX[c>M] = -np.inf # infeasible choices should have -inf (just in case)\n",
" V1 = np.amax(MX,axis=0,keepdims=False) # optimal choice as maximum in every column, 1-dim vector\n",
" c1 = c[np.argmax(MX,axis=0),range(self.ngrid)] # choose the max attaining levels of c\n",
" return V1, c1\n",
"\n",
" def solve_vfi (self,maxiter=500,tol=1e-4,callback=None):\n",
" '''Solves the model using value function iterations (successive approximations of Bellman operator)\n",
" Callback function is invoked at each iteration with keyword arguments.\n",
" '''\n",
" V0 = self.utility(self.grid) # on first iteration assume consuming everything\n",
" for iter in range(maxiter):\n",
" V1,c1 = self.bellman_discretized(V0)\n",
" err = np.amax(np.abs(V1-V0))\n",
" if callback: callback(iter=iter,model=self,value=V1,policy=c1,err=err) # callback for making plots\n",
" if err < tol:\n",
" break # converged!\n",
" V0 = V1 # prepare for the next iteration\n",
" else: # when iter went up to maxiter\n",
" raise RuntimeError('No convergence: maximum number of iterations achieved!')\n",
" return V1,c1\n",
"\n",
" def solve_plot(self,solver,**kvarg):\n",
" '''Illustrate solution\n",
" Inputs: solver (string), and any inputs to the solver\n",
" '''\n",
" if solver=='vfi':\n",
" solver_func = self.solve_vfi\n",
" else:\n",
" raise ValueError('Unknown solver label')\n",
" fig1, (ax1,ax2) = plt.subplots(1,2,figsize=(14,8))\n",
" ax1.grid(b=True, which='both', color='0.65', linestyle='-')\n",
" ax2.grid(b=True, which='both', color='0.65', linestyle='-')\n",
" ax1.set_title('Value function convergence with %s'%solver)\n",
" ax2.set_title('Policy function convergence with %s'%solver)\n",
" ax1.set_xlabel('Wealth, M')\n",
" ax2.set_xlabel('Wealth, M')\n",
" ax1.set_ylabel('Value function')\n",
" ax2.set_ylabel('Policy function')\n",
" def callback(**kwargs):\n",
" print('|',end='')\n",
" grid = kwargs['model'].grid\n",
" v = kwargs['value']\n",
" c = kwargs['policy']\n",
" ax1.plot(grid[1:],v[1:],color='k',alpha=0.25)\n",
" ax2.plot(grid,c,color='k',alpha=0.25)\n",
" V,c = solver_func(callback=callback,**kvarg)\n",
" # add solutions\n",
" ax1.plot(self.grid[1:],V[1:],color='r',linewidth=2.5)\n",
" ax2.plot(self.grid,c,color='r',linewidth=2.5)\n",
" plt.show()\n",
" return V,c"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"hide-output": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Deaton model with beta=0.900, sigma=1.000, gross return=1.050\n",
"Grids: state 2 points up to 10.0, choice 100 points, quadrature 10 points\n",
"Interpolation: linear\n",
"State grid is\n",
"[2.22044605e-16 1.00000000e+01]\n",
"Deaton model with beta=0.900, sigma=1.000, gross return=1.050\n",
"Grids: state 3 points up to 10.0, choice 100 points, quadrature 10 points\n",
"Interpolation: linear\n",
"Now state grid is\n",
"[2.22044605e-16 5.00000000e+00 1.00000000e+01]\n",
"Deaton model with beta=0.900, sigma=1.000, gross return=1.050\n",
"Grids: state 50 points up to 10.0, choice 100 points, quadrature 2 points\n",
"Interpolation: linear\n",
"Quadrature points and weights are\n",
"[[[0.44850623 2.22962345]]]\n",
"[0.5 0.5]\n",
"Deaton model with beta=0.900, sigma=1.000, gross return=1.050\n",
"Grids: state 50 points up to 10.0, choice 100 points, quadrature 4 points\n",
"Interpolation: linear\n",
"Now quadrature points and weights are\n",
"[[[0.22762954 0.64410921 1.55253176 4.39310303]]]\n",
"[0.17392742 0.32607258 0.32607258 0.17392742]\n"
]
}
],
"source": [
"# check automatically updating grids:\n",
"m = deaton(ngrid=2)\n",
"print(m,'State grid is',m.grid,sep='\\n')\n",
"m.ngrid = 3\n",
"print(m,'Now state grid is',m.grid,sep='\\n')\n",
"\n",
"# check automatically updating quadrature points and weights:\n",
"m = deaton(nquad=2)\n",
"print(m,'Quadrature points and weights are',m.quadp,m.quadw,sep='\\n')\n",
"m.nquad = 4\n",
"print(m,'Now quadrature points and weights are',m.quadp,m.quadw,sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"hide-output": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"||||||||||||||"
]
},
{
"data": {
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\n",
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