{ "cells": [ { "cell_type": "markdown", "id": "3e9d03ef", "metadata": {}, "source": [ "\"Open" ] }, { "cell_type": "markdown", "id": "0235a5e1", "metadata": {}, "source": [ "# Notebook: Discrete Time\n", "\n", "Shock elasticities quantify the (local) exposures of macroeconomic cash flows to shocks over alternative investment horizons and the corresponding prices or investors’ compensations. Here we cover shock elasticities for models that are exponential-quadratic. This model structure is particularly tractable with quasi-analytical solutions. This notebook implements methods and formulas developed in [[2]](https://larspeterhansen.org/wp-content/uploads/2016/10/macroterm_main.pdf) and [[3]](https://larspeterhansen.org/wp-content/uploads/2016/10/Examining-Macroeconomic-Models-through-the-Lens-of-Asset-Pricing.pdf). \n", "- Section 1 introduces the exponential–quadratic framework. It supposes this structure emerges as an approximation with the approximation taken as input into the computer code. \n", "- Section 2 presents shock elasticity formulas for both exposure and price elasticities.\n", "- Section 3 provides an illustration using the long-run risk model [[1]](https://www.jstor.org/stable/3694869). \n", " \n", "This notebook provides both written explanations and accompanying code. The simplest way to run the code is to open the Google Colab link at the top of this page. Otherwise, the reader can copy the code snippets onto their local machine.\n", "\n", "" ] }, { "cell_type": "markdown", "id": "47f73d3b", "metadata": {}, "source": [ "\n", "\n", "# 1. Exponential-linear-quadratic Framework\n", "\n", "We suppose a linear-quadratic specification of the state dynamics:\n", "\n", "$$\n", "\\begin{align}\n", "X_{t}^0&= \\bar{x} \\notag \\\\\n", "X_{t+1}^1&=\\Theta_{10}^x+\\Theta_{11}^x X_{t}^1+\\Sigma_{10}^x W_{t+1} \\tag{1} \\\\\n", "X_{t+1}^2&= \\Theta_{20}^x+\\Theta_{21}^x X_{t}^1+\\Theta_{22}^xX_{t}^2+\\Theta_{23}^x\\left(X_{t}^1 \\otimes X_{t}^1\\right) +\\Sigma_{20}^x W_{t+1}+\\Sigma_{21}^x\\left(X_{t}^1 \\otimes W_{t+1}\\right) +\\Sigma_{22}^x\\left(W_{t+1} \\otimes W_{t+1}\\right) \\notag .\n", "\\end{align} \n", "$$\n", "\n", "The code will take the coefficients as inputs.\n", "\n", "One way to construct such a system is to consider a family of the dynamic systems \n", "\n", "$$\n", "X_{t+1}(\\sf{q})=\\psi \\left[X_t(\\sf{q}), \\sf{q} W_{t+1}, \\sf{q}\\right]\n", "$$\n", "\n", "parameterized by $\\sf q$. We calculate a second-order approximation of stochastic processes\n", "around the ``small noise'' limit ${\\sf q} = 0$:\n", "\n", "$$\n", "X_t(\\mathrm {q}) \\approx X_{0, t}+ {\\sf q} X_{1, t}+\\frac{{\\sf q}^2}{2} X_{2, t}\n", "$$\n", "\n", "where $X_{t}^m$ is the date $t$, $m^{th}$-order derivatitve approximation. This gives a version of the system where \n", "\n", "$$ \n", "\\begin{aligned}\n", "\\bar{x} = &\\ \\psi(\\bar x, 0, 0 ) \\\\\n", "\\Theta_{11}^x = &\\ \\psi_1 \\\\\n", "\\Sigma_{10}^x = &\\ \\psi_2\\\\\n", "\\Theta_{10}^x = &\\ \\psi_3 \n", "\\end{aligned} \n", "$$ \n", "\n", "and $\\psi_i$ is the derivative of $\\psi$ with respect to argument $i$ evaluated at $(\\bar x, 0, 0)$. There are analogous formulas for the remaining matrices. \n", "\n", "For the purposes of the code we take the dynamic state evolution as a starting point with state vector \n", "\n", "$$\n", "X_t = \\begin{bmatrix} X_{t}^0 \\cr X_{t}^1 \\cr X_{t}^2 \\end{bmatrix}. \n", "$$\n", "\n", "as the Markov state with evolution.\n", "\n", "We suppose that the logarithms of macroeconomic and stochastic discount factor processes that interest us grow or decay stochastically over time with stationary increments. Let $Y$ be the logarithm of such a process. The process $Y$ will display linear growth or decay. We refer to its exponential $M = \\exp(Y)$ as a multiplicative process. We use multiplicative processes to capture growth or decay in levels. We presume that \n", "\n", "$$\n", "Y_{t+1} - Y_t = \\Theta_0^y + \\Theta_{1}^y X_{1,t} + \\Theta_{2}^y X_{2,t} + \\left( X_{1,t} \\right)' \\Theta_{3}^y X_{1,t} + \\Sigma_0^y W_{t+1} + \\left(X_{1,t} \\right)' \\Sigma_1^y W_{t+1} + \\left( W_{t+1} \\right)' \\Sigma_2^y W_{t+1} \\tag{2}\n", "$$\n", "\n", "\n", "Again we could derive this by embedding the $Y$ process in a parameterized family of processes with increments given by \n", "\n", "$$\n", "Y_{t+1}(\\sf q)-Y_t({\\sf q})=\\kappa\\left[X_t({\\sf q}), {\\sf q} W_{t+1}, {\\sf q}\\right]\n", "$$ \n", "\n", "We approximate the resulting processes as: \n", "\n", "$$\n", "Y_{t+1}(\\mathrm q)-Y_t({\\sf q}) \\approx Y_{t+1}^0-Y_{t}^0+ {\\sf q} (Y_{t+1}^1 -Y_{t}^1)+\\frac{{\\sf q}^2}{2} (Y_{t+1}^2-Y_{t}^2)\n", "$$ \n", "\n", "Following the steps of our approximation of $X$, we write \n", "\n", "$$\n", "\\begin{aligned} \n", "\\Theta_0^y &= \\kappa\\left({\\bar x},0,0 \\right) & \\Sigma_0^y &= \\kappa_2 \\\\\n", "\\Theta_1^y &= \\kappa_1 & \\Sigma_1^y &= \\kappa_{12} \\\\\n", "\\Theta_2^y &= \\kappa_{1} & \\Sigma_2^y &= \\frac 1 2 \\kappa_{22} \\\\ \n", "\\Theta_3^y &= \\frac 1 2 \\kappa_{1,1} & &\n", "\\end{aligned}\n", "$$ \n", "\n", "where $\\kappa_i$ is the derivative of $\\kappa$ with respect to argument $i$ evaluated at $(\\bar x, 0, 0)$ and similarly for the second derivatives. We set ${\\sf q} = 1$. \n", "\n", "In what follows, $M$ will be a macro growth process, a stochastic discount factor process, or a product of the two. The user inputs are the quadratic specifications in equation (1) and equation (2). " ] }, { "cell_type": "code", "execution_count": 1, "id": "ab4e62fc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'c': array([[0.0015]]),\n", " 'x': array([[1., 0.]]),\n", " 'w': array([[0. , 0. , 0.0078, 0. ]]),\n", " 'x2': array([[0.5, 0. ]]),\n", " 'xw': array([[0. , 0. , 0. , 0. , 0. , 0. , 0.0039, 0. ]])}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import sys\n", "workdir = os.getcwd()\n", "# !git clone https://github.com/lphansen/RiskUncertaintyValue # Please uncomment this line when running on the google colab\n", "# workdir = os.getcwd() + '/RiskUncertaintyValue' # Please uncomment this line when running on the google colab\n", "sys.path.insert(0, workdir+'/src')\n", "from IPython.display import display, HTML\n", "display(HTML(\"\"))\n", "import numpy as np\n", "np.set_printoptions(suppress=True)\n", "from scipy.stats import norm\n", "from numba import njit, prange\n", "\n", "from lin_quad import LinQuadVar\n", "from lin_quad_util import next_period, log_E_exp, kron_prod, distance\n", "from utilities import mat, vec, sym\n", "from elasticity import exposure_elasticity, price_elasticity\n", "\n", "\"\"\"\n", "Python object 'LinQuadVar' stores the coefficients of a variable that has the linear-quadratic structure as shown in equations (1) and (2).\n", "Below is an example of constructing a variable using the 'LinQuadVar'.\n", "\"\"\"\n", "\n", "n_Y = 1 # The dimension of the LinQuadVar, here we construct a one dimensional (scalar) LinQuadVar\n", "n_X = 2 # The dimension of state variables, X_t\n", "n_W = 4 # The dimension of shocks, W_t\n", "lq = LinQuadVar({'c': np.array([[0.0015]]), # The constant term\n", " 'x': np.array([[1., 0.]]), # The coefficients on X_{1,t}\n", " 'w': np.array([[0., 0., 0.0078, 0. ]]), # The coefficients on W_{t+1}\n", " 'x2': np.array([[0.5, 0.]]), # The coefficients on X_{2,t}\n", " 'xw': np.array([[0., 0., 0., 0., 0., 0., 0.0039, 0.]])}, # The coefficients on X_{1, t} \\otimes W_{t+1}\n", " shape = (n_Y, n_X, n_W)) # The dimensions of LinQuadVar, X_t, and, W_t\n", "\n", "## Display the coefficients of the LinQuadVar\n", "lq.coeffs" ] }, { "cell_type": "markdown", "id": "e0b2840c", "metadata": {}, "source": [ "# 2. Shock Elasticity\n", "## 2.1 Analytical framework\n", "Shock elasticities are used to quantify the date $t$ impact on values of exposure to the shock $\n", "(\\alpha_0 + \\alpha_1 X_0) \\cdot W_1$ at date one. It has the form shown in equation 3: \n", "\n", "$$\n", "\\varepsilon( x, t)= \\frac{(\\alpha_0 + \\alpha_1 x) \\cdot {\\mathbb E}\\left[\\left( \\frac {M_t}{M_0}\\right) W_1 \\mid X_0 = x\\right]}{{\\mathbb E} \\left(\\frac {M_t}{M_0} \\mid X_0 = x\\right)}\\tag{3}\n", "$$\n", "\n", "Using the linear-quadratic dynamics of section 1, the computer software computes the logarithm of the denominator of formula (3): \n", "\n", "$$\n", "\\log {\\mathbb E}\\left(\\frac {M_t}{M_1} \\mid X_1 =x\\right) = \\Phi_{0, t}^*+\\Phi_{1, t}^* x_1 +\\Phi_{2, t}^*x_2 +\n", "{\\frac 1 2} (x_1)' \\Phi_{3, t}^*x_1 \\tag{4}\n", "$$\n", "\n", "recursively. Form \n", "\n", "$$\n", "{\\mathbb M} \\left[ \\Phi_{0} +\\Phi_1 x_1+\\Phi_2 x_2 + {\\frac 1 2} (x_1)'\\Phi_3 x_1\\right] \\equiv\n", "\\log {\\mathbb E} \\left[\\left( \\frac {M_2}{M_1}\\right)\\exp\\left[\\Phi_{0} +\\Phi_1 x_1+\\Phi_2 x_2 + {\\frac 1 2} (x_1)'\\Phi_3 x_1\\right] \\mid X_0 = x \\right] \n", "$$ \n", "\n", "Conveniently, we can express the outcomes as: \n", "\n", "$$\n", "{\\mathbb M} \\left[ \\Phi_{0} +\\Phi_1 x_1+\\Phi_2 x_2 + {\\frac 1 2} (x_1)'\\Phi_3 x_1\\right] =\n", "{\\widetilde \\Phi}_{0} + {\\widetilde \\Phi}_1 x_1+ {\\widetilde \\Phi}_2 x_2 + {\\frac 1 2} (x_1)'{\\widetilde \\Phi}_3 x_1\n", "$$ \n", "\n", "for some specification of ${\\widetilde \\Phi}_i$, $i=0,1,2,3$. In words, the ${\\mathbb M}$ operator maps linear-quadratic functions into linear-quadratic functions. The code uses this mapping repeatedly. \n", "\n", "By a direct application of the Law of Iterated Expectations we have that \n", "\n", "$$\n", "\\log {\\mathbb E}\\left(\\frac {M_t}{M_1} \\mid X_1 =x\\right) = {\\mathbb M}^{t-1}[1]\n", "$$ \n", "\n", "where ${\\mathbb M}^{t-1}[1]$ means to apply the operator ${\\mathbb M}$ $t-1$ times in succession to a function that is identically one. Observe that ${\\mathbb M}^{t-1}[1]$ is a function of $x$. \n", "\n", "The function *_Φ_star* defined below calculates the linear-quadratic dynamic coefficients in $\\mathbb{M}$ mappings and iterations.\n", "\n", "To complete the calculation of the elasticity, \n", "note that \n", "\n", "$$\n", "\\frac{{\\mathbb E}\\left[\\left(\\frac { M_t}{M_0}\\right) W_1 \\mid X_0=x\\right]}{{\\mathbb E}\\left[\\left(\\frac {M_t}{M_0}\\right) \\mid X_0=x\\right]}=\n", "\\frac{{\\mathbb E}\\left[ \\left(\\frac {M_1}{M_0}\\right) {\\mathbb E}\\left(\\frac{M_t}{M_1} \\mid X_1\\right) W_1 \\mid X_0=x\\right]}{{\\mathbb E}\\left[\\left(\\frac {M_1}{M_0}\\right) E\\left(\\frac{M_t}{M_1} \\mid X_1\\right) \\mid X_0=x\\right]} .\n", "$$ \n", "\n", "This leads us to construct the nonegative random variable: \n", "\n", "$$\n", "L_{t}\\equiv\\frac{\\left(\\frac {M_1}{M_0}\\right) \\exp\\left[ {\\mathbb M}^{t-1}[1](X_1) \\right]}{{\\mathbb E}\\left[\\left(\\frac {M_1}{M_0}\\right) \\exp\\left[ {\\mathbb M}^{t-1}[1](X_1) \\right]\n", " \\mid X_0=x\\right]} \n", "$$ \n", "\n", "Notice that $L_{t}$ depends only date one information and has expectation one conditioned on date zero information. Multiplying this positive random\n", "variable by $W_1$ and taking expectations is equivalent to changing the conditional probability distribution and evaluating the conditional expectation of $W_1$ under this change of measure. Since $W_1$ is normally distributed, the exponential quadratic construction of $L_{t}$ implies that $W_1$ remains normally distributed but with a different mean and covariance matrix. The computer codes use this observation to evaluate formula (5) by taking an altered conditional expectation of $W_1$. The derivation details to compute the conditional moments under the change of probability measure implied by $L_t$ can be found in [2], Appendix B, and [3], Appendix A.\n", "\n", "For the purposes of the code, denote the conditional mean induced by $L_{t}$ as $\\mu_{t}^0 + \\mu_{t}^1 x_1$ and the conditional covariance matrix ${\\widetilde \\Sigma}_t$.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "559eac2f", "metadata": {}, "outputs": [], "source": [ "def _Φ_star(log_M_growth, X1_tp1, X2_tp1, T):\n", " r\"\"\"\n", " Computes :math:`\\Phi^*_{0,t-1}`, :math:`\\Phi^*_{1,t-1}`, :math:`\\Phi^*_{2,t-1}`, :math:`\\Phi^*_{3,t-1}` in equation (4).\n", "\n", " Parameters\n", " ----------\n", " log_M_growth : LinQuadVar\n", " Log growth of multiplicative functional M.\n", " e.g. log consumption growth, :math:`\\log \\frac{C_{t+1}}{C_t}`\n", " X1_tp1 : LinQuadVar\n", " Stores the coefficients of laws of motion for X1.\n", " X2_tp2 : LinQuadVar or None\n", " Stores the coefficients of laws of motion for X2.\n", " T : int\n", " Time horizon.\n", "\n", " Returns\n", " -------\n", " Φ_star_1tm1_all : (T, 1, n_X) ndarray\n", " Φ_star_2tm1_all : (T, 1, n_X) ndarray\n", " Φ_star_3tm1_all : (T, 1, n_X**2) ndarray\n", "\n", " \"\"\"\n", " _, n_X, _ = X1_tp1.shape\n", " \n", " Φ_star_1tm1_all = np.zeros((T, 1, n_X))\n", " Φ_star_2tm1_all = np.zeros((T, 1, n_X))\n", " Φ_star_3tm1_all = np.zeros((T, 1, n_X**2))\n", " log_M_growth_distort = log_E_exp(log_M_growth)\n", " X1X1 = kron_prod(X1_tp1, X1_tp1)\n", "\n", " for i in range(1, T):\n", " Φ_star_1tm1_all[i] = log_M_growth_distort['x']\n", " Φ_star_2tm1_all[i] = log_M_growth_distort['x2']\n", " Φ_star_3tm1_all[i] = log_M_growth_distort['xx']\n", " temp = next_period(log_M_growth_distort, X1_tp1, X2_tp1, X1X1)\n", " log_M_growth_distort = log_E_exp(log_M_growth + temp)\n", "\n", " return Φ_star_1tm1_all, Φ_star_2tm1_all, Φ_star_3tm1_all" ] }, { "cell_type": "markdown", "id": "4a8f3acc", "metadata": {}, "source": [ "## 2.2 Limiting Behavior\n", "\n", "The operator ${\\mathbb M}$ typically has a solution to the following equation: \n", "\n", "$$\n", "{\\mathbb M} \\left[\\Phi_1^e x_1+\\Phi_2^e x_2 + {\\frac 1 2} (x_1)'\\Phi_3^e x_1\\right] \n", "= \n", "\\eta^e + \n", " \\left[ \\Phi_1^e x_1+\\Phi_2^e x_2 + {\\frac 1 2} (x_1)'\\Phi_3^e x_1\\right] \\tag{6} \n", "$$\n", "\n", "\n", "The solution of interest when, it exists, can be deduced by iterating on the ${\\mathbb M}$ operator allowing for a constant shift $\\eta^e$. This solution gives a characterization of the limiting elasticity. Construct \n", "\n", "$$\n", "L_{1,\\infty} = \\left(\\frac {M_1}{M_0} \\right)\\left(\\frac{\\exp\\left[\\Phi_1^e X_{1,1} +\\Phi_2^e X_{2,1} + {\\frac 1 2} (X_{1,1})'\\Phi_3^e X_{1,1} \\right]}{ \n", "\\exp(\\eta) \\exp\\left[ \\Phi_1^e X_{1,0} +\\Phi_2^e X_{2,0} + {\\frac 1 2} (X_{1,0})'\\Phi_3^e X_{1,0}\\right]} \\right) . \n", "$$ \n", "\n", "The elasticities are given by conditional linear combinations of conditional expectations of $W_1$ under this limiting change of measure.\n", "\n", "To express the equation of interest differently, consider the operator, ${\\mathbb P}$ that maps $\\Phi_{0} +\\Phi_1 x_1+\\Phi_2 x_2 + {\\frac 1 2} (x_1)'\\Phi_3 x_1$ into $\\exp\\left({\\mathbb M} \\left[ \\Phi_{0} +\\Phi_1^e x_1+\\Phi_2 x_2 + {\\frac 1 2} (x_1)'\\Phi_3 x_1\\right] \\right)$. Rewrite equation (6) as: \n", "\n", "$$\n", "{\\mathbb P} \\left[ \\Phi_1^e x_1+\\Phi_2^e x_2 + {\\frac 1 2} (x_1)'\\Phi_3^e x_1\\right] \n", "= \n", "\\exp(\\eta) \n", " \\left[ \\Phi_1^e x_1+\\Phi_2^e x_2 + {\\frac 1 2} (x_1)'\\Phi_3^e x_1\\right]\n", "$$ \n", "\n", "which is an eigenvalue equation where $\\exp\\left[ \\Phi_1^e x_1+\\Phi_2^e x_2 + {\\frac 1 2} (x_1)'\\Phi_3^e x_1\\right]$ is a postive eigenfunction and $\\exp(\\eta)$ is a positive eigenvalue. [^1] \n", "\n", "\n", "The codes below solve the eigenvalue problem using the *M_mapping*.\n", "\n", "[^1]: This eigenvalue problem is the so called Perron-Frobenius problem. " ] }, { "cell_type": "code", "execution_count": 3, "id": "ea24e04e", "metadata": {}, "outputs": [], "source": [ "def M_mapping(log_M_growth, f, X1_tp1, X2_tp1, second_order = True):\n", " r'''\n", " Computes coefficients of a LinQuadVar after one iteration of M mapping\n", "\n", " Parameters\n", " ----------\n", " log_M_growth : LinQuadVar\n", " Log difference of multiplicative functional.\n", " e.g. log consumption growth, :math:`\\log \\frac{C_{t+1}}{C_t}`\n", " f : LinQuadVar\n", " The function M Mapping operate on. \n", " e.g. A function that is identically one, log_f = LinQuadVar({'c': np.zeros((1,1))}, log_M_growth.shape)\n", " X1_tp1 : LinQuadVar \n", " Stores the coefficients of laws of motion for X1.\n", " X2_tp2 : LinQuadVar or None\n", " Stores the coefficients of laws of motion for X2. \n", " second_order: boolean\n", " Whether the second order expansion of the state evoluton equation has been input\n", " \n", " Returns\n", " -------\n", " LinQuadVar, stores the coefficients of the new LinQuadVar after one iteration of M Mapping\n", " '''\n", " if second_order:\n", " return log_E_exp(log_M_growth + next_period(f, X1_tp1, X2_tp1))\n", " else:\n", " if X2_tp1 != None:\n", " print('The second order expansion for law of motion is not used in the first order expansion.')\n", " return log_E_exp(log_M_growth + next_period(f, X1_tp1))\n", " \n", "def Q_mapping(log_M_growth, f, X1_tp1, X2_tp1, tol = 1e-10, max_iter = 10000, second_order = True):\n", " r'''\n", " Computes limiting coefficients of a LinQuadVar by recursively applying the M mapping operator till convergence, returns the eigenvalue and eigenvector.\n", "\n", " Parameters\n", " ----------\n", " log_M_growth : LinQuadVar\n", " Log difference of multiplicative functional.\n", " e.g. log consumption growth, :math:`\\log \\frac{C_{t+1}}{C_t}`\n", " f : LinQuadVar\n", " The function M Mapping operate on. \n", " e.g. A function that is identically one, log_f = LinQuadVar({'c': np.zeros((1,1))}, log_M_growth.shape)\n", " X1_tp1 : LinQuadVar \n", " Stores the coefficients of laws of motion for X1.\n", " X2_tp2 : LinQuadVar or None\n", " Stores the coefficients of laws of motion for X2. \n", " tol: float\n", " tolerance for convergence\n", " max_iter: int\n", " maximum iteration\n", " second_order: boolean\n", " Whether the second order expansion of the state evoluton equation has been input\n", "\n", " Returns\n", " -------\n", " Qf_components_log : List of LinQuadVar\n", " stores the coefficients of the LinQuadVar in each iteration of M Mapping\n", " f: LinQuadVar\n", " The function M Mapping operate on. \n", " e.g. A function that is identically one, log_f = LinQuadVar({'c': np.zeros((1,1))}, log_M_growth.shape)\n", " η: float\n", " The eigenvalue\n", " η_series: list of float\n", " The convergence path of the eigenvalue \n", " '''\n", " η_series = []\n", " Qf_components_log = []\n", " for i in range(max_iter):\n", " Qf_components_log.append(f)\n", " if second_order:\n", " f_next = M_mapping(log_M_growth, f, X1_tp1, X2_tp1, second_order = second_order)\n", " else:\n", " if X2_tp1 != None:\n", " print('The second order expansion for law of motion is not used in the first order expansion.')\n", " f_next = M_mapping(log_M_growth, f, X1_tp1, second_order = second_order)\n", " η = (f_next['c'] - f['c']).item()\n", " η_series.append(η)\n", " \n", " if distance(f, f_next, ['x', 'xx', 'x2']) < tol:\n", " break\n", " f = f_next\n", " \n", " if i >= max_iter-1:\n", " print(\"Warning: Q iteration may not have converged.\")\n", " Qf_components_log.append(f_next)\n", " \n", " return Qf_components_log, f, η, η_series" ] }, { "cell_type": "markdown", "id": "112b5830", "metadata": {}, "source": [ "The function `_elasticity_coeff` defined below calculates the conditional mean induced by $L_{t}$ as $\\mu_{0,t} + \\mu_{1,t} x_1$ and the covariance matrix as ${\\widetilde \\Sigma}_{t}.$" ] }, { "cell_type": "code", "execution_count": 4, "id": "b56ce56e", "metadata": {}, "outputs": [], "source": [ "def _elasticity_coeff(log_M_growth, X1_tp1, X2_tp1, T):\n", " r\"\"\"\n", " Computes :math:`\\mu_{t,0}`, :math:`\\mu_{t,1}`, :math:`\\tilde{\\Sigma}_t`. Corresponding formulas can be found in [3], Jaroslav and Hansen (2014), Appendix B.\n", "\n", " Parameters\n", " ----------\n", " log_M_growth : LinQuadVar\n", " Log difference of multiplicative functional.\n", " e.g. log consumption growth, :math:`\\log \\frac{C_{t+1}}{C_t}`\n", " X1_tp1 : LinQuadVar\n", " Stores the coefficients of laws of motion for X1.\n", " X2_tp2 : LinQuadVar or None\n", " Stores the coefficients of laws of motion for X2. \n", " T : int\n", " Time horizon.\n", "\n", " Returns\n", " -------\n", " Σ_tilde_t_all : (T, n_W, n_W) ndarray\n", " μ_t0_all : (T, n_W, 1) ndarray\n", " μ_t1_all : (T, n_W, n_X) ndarray\n", "\n", " \"\"\"\n", " _, n_X, n_W = log_M_growth.shape\n", " \n", " Φ_star_1tm1_all, Φ_star_2tm1_all, Φ_star_3tm1_all = _Φ_star(log_M_growth, X1_tp1, X2_tp1, T)\n", " Ψ_0 = log_M_growth['w']\n", " Ψ_1 = log_M_growth['xw']\n", " Ψ_2 = log_M_growth['ww']\n", " Λ_10 = X1_tp1['w']\n", " if log_M_growth.second_order:\n", " Λ_20 = X2_tp1['w']\n", " Λ_21 = X2_tp1['xw']\n", " Λ_22 = X2_tp1['ww']\n", " else:\n", " Λ_20 = np.zeros((n_X,n_W))\n", " Λ_21 = np.zeros((n_X,n_X*n_W))\n", " Λ_22 = np.zeros((n_X,n_W**2))\n", " Θ_10 = X1_tp1['c']\n", " Θ_11 = X1_tp1['x']\n", " \n", " Σ_tilde_t_all, μ_t0_all, μ_t1_all \\\n", " = _elasticity_coeff_inner_loop(Φ_star_1tm1_all, Φ_star_2tm1_all, Φ_star_3tm1_all, Ψ_0, Ψ_1, Ψ_2, Λ_10, Λ_20, Λ_21, Λ_22, Θ_10, Θ_11, n_X, n_W, T) \n", " \n", " return Σ_tilde_t_all, μ_t0_all, μ_t1_all\n", "\n", "@njit\n", "def _elasticity_coeff_inner_loop(Φ_star_1tm1_all, Φ_star_2tm1_all, Φ_star_3tm1_all, Ψ_0, Ψ_1, Ψ_2, Λ_10, Λ_20, Λ_21, Λ_22, Θ_10, Θ_11, n_X, n_W, T):\n", " \n", " Σ_tilde_t_all = np.zeros((T, n_W, n_W))\n", " μ_t0_all = np.zeros((T, n_W, 1))\n", " μ_t1_all = np.zeros((T, n_W, n_X)) \n", "\n", " kron_Λ_10_Λ_10 = np.kron(Λ_10,Λ_10)\n", " kron_Θ_10_Λ_10_sum = np.kron(Θ_10,Λ_10) + np.kron(Λ_10,Θ_10)\n", "\n", " temp = np.kron(Λ_10, Θ_11[:, 0:1].copy())\n", " for j in range(1, n_X):\n", " temp = np.hstack((temp, np.kron(Λ_10, Θ_11[:, j:j+1].copy())))\n", "\n", " kron_Θ_11_Λ_10_term = np.kron(Θ_11, Λ_10) + temp\n", "\n", " for t in prange(T):\n", " Φ_star_1tm1 = Φ_star_1tm1_all[t]\n", " Φ_star_2tm1 = Φ_star_2tm1_all[t]\n", " Φ_star_3tm1 = Φ_star_3tm1_all[t]\n", "\n", " Σ_tilde_t_inv = np.eye(n_W)- 2 * sym(mat(Ψ_2 + Φ_star_2tm1@Λ_22 + Φ_star_3tm1@kron_Λ_10_Λ_10, (n_W, n_W)))\n", " μ_t0 = (Ψ_0 + Φ_star_1tm1@Λ_10 + Φ_star_2tm1@Λ_20 + Φ_star_3tm1 @ kron_Θ_10_Λ_10_sum).T\n", " μ_t1 = mat(Ψ_1 + Φ_star_2tm1 @ Λ_21 + Φ_star_3tm1 @ kron_Θ_11_Λ_10_term,(n_W, n_X))\n", " Σ_tilde_t_all[t] = np.linalg.inv(Σ_tilde_t_inv)\n", " μ_t0_all[t] = μ_t0\n", " μ_t1_all[t] = μ_t1\n", " \n", " return Σ_tilde_t_all, μ_t0_all, μ_t1_all" ] }, { "cell_type": "markdown", "id": "cea86821", "metadata": {}, "source": [ "## 2.3 Exposure and Price Elasticities\n", "\n", "We consider two types of multiplicative processes, one that captures macroeconomic growth, denoted by $G$, and another that captures stochastic discounting, denoted by $S$. \n", "- The stochastic discount factor process, $S$, is typically computed from the underlying economic model to reflect equilibrium valuation dynamics. \n", "- For instance, the growth process $G$ might be a consumption process or some other endogenously determined cash flow, or it might be an exogenously specified technology shock process that grows through time. \n", "- The interplay between $S$ and $G$ will determine uncertainty compensations over multi-period investment horizons.\n", "\n", "Consider the pricing of a vector of payoffs $G_tW_1$ in comparison to the scalar payoff $G_t$. \n", "\n", "- The **shock-exposure elasticity** is constructed as from the ratio of expected payoffs $E[G_tW_1 |X_0 =x]$ relative to $E [G_t | X_0 = x]$. To calculate shock-exposure elasticity, the multiplicative functional $M$ is set as $G$.\n", "\n", " $$\n", " \\varepsilon_{g}( x, t)= \\frac{(\\alpha_0 + \\alpha_1 x) \\cdot {\\mathbb E}\\left[\\left( \\frac {G_t}{G_0}\\right) W_1 \\mid X_0 = x\\right]}{{\\mathbb E} \\left(\\frac {G_t}{G_0} \\mid X_0 = x\\right)}\n", " $$\n", "\n", " - This is done by the function *\\_exposure\\_elasticity*. This function uses the *\\_elasticity\\_coeff* defined in the last section. \n", "- The **shock-price elasticity** includes an adjustment for the values of the payoffs $E [S_t G_t W_1 | X_0 = x]$ relative to $E [S_t G_t | X_0 = x]$. To calculate shock-price elasticity, the multiplicative functional $M$ is set as the product $SG$. \n", "\n", "$$\n", "\\varepsilon_{sg}( x, t)= \\frac{(\\alpha_0 + \\alpha_1 x) \\cdot {\\mathbb E}\\left[\\left( \\frac {S_tG_t}{S_0G_0}\\right) W_1 \\mid X_0 = x\\right]}{{\\mathbb E} \\left(\\frac {S_tG_t}{S_0G_0} \\mid X_0 = x\\right)}.\n", "$$ \n", "\n", "The shock-price elasticity is: \n", "\n", "$$\n", "\\varepsilon_{g}( x, t)-\\varepsilon_{sg}( x, t)\n", "$$\n", "\n", " - This computation is done by the function *price\\_elasticity*. This function also uses the *\\_elasticity\\_coeff* defined in the last section. \n", " \n", "Since the shock elasticity function depends on $x_1$, the code computes percentiles of the shock elasticity based on the stationary distribution of $x_1$. This is done by the internal function *\\_compute\\_percentile* in *exposure\\_elasticity* and *price\\_elasticity*. " ] }, { "cell_type": "code", "execution_count": 5, "id": "760488f2", "metadata": {}, "outputs": [], "source": [ "def exposure_elasticity(log_M_growth, X1_tp1, X2_tp1, T=400, shock=0, percentile=0.5):\n", " r\"\"\"\n", " Computes exposure elasticity for M.\n", "\n", " Parameters\n", " ----------\n", " log_M_growth : LinQuadVar\n", " Log growth of multiplicative functional M.\n", " e.g. log consumption growth, :math:`\\log \\frac{C_{t+1}}{C_t}`\n", " X1_tp1 : LinQuadVar\n", " Stores the coefficients of laws of motion for X1.\n", " X2_tp1 : LinQuadVar\n", " Stores the coefficients of laws of motion for X2. \n", " T : int\n", " Time horizon.\n", " shock : int\n", " Position of the initial shock, starting from 0.\n", " percentile : float\n", " Specifies the percentile of the elasticities.\n", "\n", " Returns\n", " -------\n", " elasticities : (T, n_Y) ndarray\n", " Exposure elasticities.\n", "\n", " Reference\n", " ---------\n", " Borovicka, Hansen (2014). See http://larspeterhansen.org/.\n", "\n", " \"\"\"\n", " n_Y, n_X, n_W = log_M_growth.shape\n", " if n_Y != 1:\n", " raise ValueError(\"The dimension of input should be 1.\")\n", "\n", " α = np.zeros(n_W)\n", " α[shock] = 1 \n", " p = norm.ppf(percentile)\n", "\n", " Σ_tilde_t, μ_t0, μ_t1 = _elasticity_coeff(log_M_growth, X1_tp1, X2_tp1, T)\n", "\n", " kron_product = np.kron(X1_tp1['x'], X1_tp1['x'])\n", " x_mean = np.linalg.solve(np.eye(n_X)-X1_tp1['x'],X1_tp1['c'])\n", " x_cov = mat(np.linalg.solve(np.eye(n_X**2)-kron_product,\n", " vec(X1_tp1['w']@X1_tp1['w'].T)), (n_X, n_X))\n", "\n", " elasticities = _exposure_elasticity_loop(T, n_Y, α, Σ_tilde_t, μ_t0,\n", " μ_t1, percentile, x_mean, x_cov, p)\n", "\n", " return elasticities\n", "\n", "@njit(parallel=True)\n", "def _exposure_elasticity_loop(T, n_Y, α, Σ_tilde_t, μ_t0, μ_t1, percentile, x_mean, x_cov, p):\n", " elasticities = np.zeros((T, n_Y))\n", " if percentile == 0.5:\n", " for t in prange(T):\n", " elasticity = (α@Σ_tilde_t[t]@μ_t0[t])[0] +(α@Σ_tilde_t[t]@μ_t1[t]@x_mean)[0]\n", " elasticities[t] = elasticity\n", " else:\n", " for t in prange(T):\n", " elasticity = (α@Σ_tilde_t[t]@μ_t0[t])[0] +(α@Σ_tilde_t[t]@μ_t1[t]@x_mean)[0]\n", " A = α@Σ_tilde_t[t]@μ_t1[t]\n", " elasticity = _compute_percentile(A, elasticity, x_cov, p)\n", " elasticities[t] = elasticity\n", " return elasticities" ] }, { "cell_type": "code", "execution_count": 6, "id": "f353a549", "metadata": {}, "outputs": [], "source": [ "def price_elasticity(log_G_growth, log_S_growth, X1_tp1, X2_tp1, T=400, shock=0, percentile=0.5):\n", " r\"\"\"\n", " Computes price elasticity.\n", "\n", " Parameters\n", " ----------\n", " log_G_growth : LinQuadVar\n", " Log growth of multiplicative functional G.\n", " e.g. log consumption growth, :math:`\\log \\frac{C_{t+1}}{C_t}`\n", " log_S_growth : LinQuadVar\n", " Log growth of multiplicative functional S.\n", " e.g. log stochastic discount factor, :math:`\\log \\frac{S_{t+1}}{S_t}`\n", " X1_tp1 : LinQuadVar\n", " Stores the coefficients of laws of motion for X1.\n", " X2_tp2 : LinQuadVar or None\n", " Stores the coefficients of laws of motion for X2. \n", " T : int\n", " Time horizon.\n", " shock : int\n", " Position of the initial shock, starting from 0.\n", " percentile : float\n", " Specifies the percentile of the elasticities.\n", "\n", " Returns\n", " -------\n", " elasticities : (T, dim) ndarray\n", " Price elasticities.\n", "\n", " Reference\n", " ---------\n", " Borovicka, Hansen (2014). See http://larspeterhansen.org/.\n", "\n", " \"\"\"\n", " if log_G_growth.shape != log_S_growth.shape:\n", " raise ValueError(\"The dimensions of G and S do not match.\")\n", " else:\n", " n_Y, n_X, n_W = log_G_growth.shape\n", " if n_Y != 1:\n", " raise ValueError(\"The dimension of inputs should be (1, n_X, n_W)\")\n", " α = np.zeros(n_W)\n", " α[shock] = 1 \n", "\n", " p = norm.ppf(percentile)\n", "\n", " Σ_tilde_expo_t, μ_expo_t0, μ_expo_t1 \\\n", " = _elasticity_coeff(log_G_growth, X1_tp1, X2_tp1, T)\n", " Σ_tilde_value_t, μ_value_t0, μ_value_t1\\\n", " = _elasticity_coeff(log_G_growth+log_S_growth, X1_tp1, X2_tp1, T)\n", "\n", " kron_product = np.kron(X1_tp1['x'], X1_tp1['x'])\n", " x_mean = np.linalg.solve(np.eye(n_X)-X1_tp1['x'],X1_tp1['c'])\n", " x_cov = mat(np.linalg.solve(np.eye(n_X**2)-kron_product,\n", " vec(X1_tp1['w']@X1_tp1['w'].T)), (n_X, n_X))\n", " \n", " elasticities = _price_elasticity_loop(T, n_Y, α, Σ_tilde_expo_t, Σ_tilde_value_t, \n", " μ_expo_t0, μ_value_t0, μ_expo_t1, μ_value_t1,\n", " percentile, x_mean, x_cov, p)\n", "\n", " return elasticities\n", "\n", "@njit(parallel=True)\n", "def _price_elasticity_loop(T, n_Y, α, Σ_tilde_expo_t, Σ_tilde_value_t, \n", " μ_expo_t0, μ_value_t0, μ_expo_t1, μ_value_t1,\n", " percentile, x_mean, x_cov, p):\n", " elasticities = np.zeros((T, n_Y))\n", " if percentile == 0.5:\n", " for t in prange(T):\n", " elasticity = (α @ (Σ_tilde_expo_t[t] @ μ_expo_t0[t] \\\n", " - Σ_tilde_value_t[t] @ μ_value_t0[t]))[0]\\\n", " +(α@(Σ_tilde_expo_t[t]@μ_expo_t1[t]@x_mean\\\n", " - Σ_tilde_value_t[t] @ μ_value_t1[t]@x_mean))[0]\n", " elasticities[t] = elasticity \n", " else:\n", " for t in prange(T):\n", " elasticity = (α @ (Σ_tilde_expo_t[t] @ μ_expo_t0[t]\\\n", " - Σ_tilde_value_t[t] @ μ_value_t0[t]))[0]\\\n", " +(α@(Σ_tilde_expo_t[t]@μ_expo_t1[t]@x_mean\\\n", " - Σ_tilde_value_t[t] @ μ_value_t1[t]@x_mean))[0]\n", " A = α @ (Σ_tilde_expo_t[t]@μ_expo_t1[t]\\\n", " - Σ_tilde_value_t[t]@μ_value_t1[t])\n", " elasticity = _compute_percentile(A, elasticity, x_cov, p)\n", " elasticities[t] = elasticity\n", " return elasticities" ] }, { "cell_type": "code", "execution_count": 7, "id": "7d09b2d5", "metadata": {}, "outputs": [], "source": [ "@njit\n", "def _compute_percentile(A, Ax_mean, x_cov, p):\n", " r\"\"\"\n", " Compute percentile of the scalar Ax, where A is vector coefficient and x follows multivariate normal distribution.\n", " \n", " Parameters\n", " ----------\n", " A : (N, ) ndarray\n", " Coefficient of Ax.\n", " Ax_mean : float\n", " Mean of Ax.\n", " x_cov : (N, N) ndarray\n", " Covariance matrix of x.\n", " p : float\n", " Percentile of a standard normal distribution.\n", "\n", " Returns\n", " -------\n", " res : float\n", " Percentile of Ax.\n", "\n", " \"\"\"\n", " Ax_var = A@x_cov@A.T\n", " Ax_std = np.sqrt(Ax_var)\n", " res = Ax_mean + Ax_std * p\n", " return res" ] }, { "cell_type": "markdown", "id": "976cc8d4", "metadata": {}, "source": [ "# 3. An Illustration using the Long-Run Risk Model [1]\n", "\n", "This example uses the long-run risk model numerical results in the exponential-linear–quadratic framework to calculate shock elasticities. The variables are expressed in Python *LinQuadVar* objects, which store the coefficients in linear-quadratic structure. The *LinQuadVar* allows the add and multiply operations. Using coefficients from [1], we form the approximation: \n", "\n", "$$\n", "\\begin{aligned}\n", "X^0_t = & \\begin{bmatrix}0\\\\1 \\end{bmatrix}\\cr\n", "X_{t+1}^1 = & \\begin{bmatrix} 0.979 & 0 \\\\ 0 & 0.987 \\end{bmatrix} X_{t}^1 + \\begin{bmatrix} 0.0343 & 0 & 0 & 0 \\\\ 0 & .0378 & 0 & 0 \\end{bmatrix} W_{t+1} \\cr\n", "X_{t+1}^2 = & \\begin{bmatrix} 0.979 & 0 \\\\ 0 & 0.987 \\end{bmatrix} X_{t}^2 + \\begin{bmatrix} 0 & 0 & 0 & 0 & 0.0343 & 0 & 0 & 0 \\\\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\end{bmatrix} X_{t}^2\\otimes W_{t+1}\n", "\\end{aligned}, \n", "$$ \n", "\n", "and the log consumption is approximately \n", "\n", "$$\n", "{\\widehat C}_{t+1} - {\\widehat C}_t = .01 \\times \\left(\\begin{bmatrix} 0.15\\end{bmatrix} + \\begin{bmatrix} 1 & 0 \\end{bmatrix} X_{t}^1 + \\begin{bmatrix}0 & 0 & .78 & 0 \\end{bmatrix}W_{t+1} + \n", "\\begin{bmatrix} .5 & 0 \\end{bmatrix} X_{t}^2 + {X_{t}^1}' \\begin{bmatrix} 0 & 0 & 0 & 0 \\\\ 0 & 0 & 0.39 & 0 \\end{bmatrix}W_{t+1} \\right)\n", "$$\n", "\n", "## 3.1 Exposure Elasticity for Consumption Growth\n", "\n", "To calculate the exposure elasticity for consumption growth using the *exposure\\_elasticity* defined above, we need six inputs\n", "- Consumption growth, *gc\\_tp1*, loaded from outside solutions. This is a *LinQuadVar* object. \n", "- First order expansion of the state evolution equations, *X1\\_tp1*, loaded from outside solutions. This is a *LinQuadVar* object. \n", "- Second order expansion of the state evolution equations, *X2\\_tp1*, loaded from outside solutions. This is a *LinQuadVar* object. \n", "- Time periods, $\\text{T} = 360$, 30 years\n", "- Shock index, $0$ stands for the growth shock, which means $\\alpha' =\\begin{bmatrix}1 & 0 & 0 & 0 \\end{bmatrix}$, $1$ stands for the volatility shock, $2$ stands for the consumption shock. The fourth shock alters dividend growth and its shock prices are zero. \n", "- Percentile, $0.5$ stands for the median" ] }, { "cell_type": "code", "execution_count": 8, "id": "122c0913", "metadata": {}, "outputs": [], "source": [ "import pickle\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "pd.options.display.float_format = '{:.3g}'.format\n", "sns.set(font_scale = 1.5)\n", "\n", "\"\"\"\n", "load the long-run risk model solutions when ρ = 2/3, 1, 1.5, 10\n", "\"\"\"\n", "\n", "with open(workdir + '/data/res_006.pkl', 'rb') as f:\n", " res_006 = pickle.load(f)\n", "with open(workdir + '/data/res_010.pkl', 'rb') as f:\n", " res_010 = pickle.load(f)\n", "with open(workdir + '/data/res_015.pkl', 'rb') as f:\n", " res_015 = pickle.load(f)\n", "with open(workdir + '/data/res_100.pkl', 'rb') as f:\n", " res_100 = pickle.load(f)\n", "\n", "# Compute the shock elasticity for 30 years\n", "T = 360\n", "# Calculate the shock elasticity at 0.25, 0.5 and 0.75 quantile\n", "quantile = [0.25, 0.5, 0.75]" ] }, { "cell_type": "code", "execution_count": 9, "id": "83d52ecf", "metadata": {}, "outputs": [ { "data": { "image/png": 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JvowDCBpOp7RypftXp0cPR657oYu+kyRl9RsgWa15nv3+t0U6nXFaNaJqqlftPv4PFgAAAF6j5gEAAAAQDKhdAAAAAAQKS2kHAASbrVstOnPGUHS0qXbtXLnuhS5aIEnKvGxgvs9mH8s5rOk/ZLXkbVQDAAAAAAAAAAAAAAAAghGNaEAR/fijeze0iy92yvaXPQWtu3fJtnePzJAQ2fv0y/PcyfSTWvLbIknSPziWEwAAAAAAAAAAAAAAAGWI10dzPvnkk0UabxiGXnrpJW+XAwLGihXuncwKOpbT3q27zAoV8zz37c6v5XA51LZqOzWLbe7/QAEAAFAs1DwAAAAAggG1CwAAAIBA4XUj2syZM2UYhkzTzHPPMIxc/26aJoUNyoTMTGnNmuxGNGeue6GL3Y1oWZddnu+zX/36hSTpH83ZDQ0AACAYUPMAAAAACAbULgAAAAAChdeNaEOGDMlTwGRLS0vT8ePHtXXrVjkcDt1+++1q2LCh10ECgWL9eqvS0w1VqeLShRe6cq4bZ04rJOFnSVLmgIF5ntt+apu2nNysEEuIrmkyrMTiBQAAgPeoeQAAAAAEA2oXAAAAAIHC60a0V1555bxjjh49qvvuu0+zZs3SnDlzvF0KCBg//vjnbmh/retDl34vw+mU48IWctWrn+e5b379UpI0oP5AxYZXLolQAQAAUEzUPDgf05RSU91/ORylHU3ZYbNJoaHk1dfIq++RU/8gr/5BXv2DvPqeL3IaGSkV0JNVZlG7AAAAAAgUXjeieaJGjRp68803NWjQIL377rt6/vnn/bkc4HcrVrh/ZQo8ljOf3dBcpkszd02XJA1r+g8/RwgAAICSRM1TfpmmNHBguBISJCmqtMMpo8irf5BX3yOn/kFe/YO8+gd59T3vc9q5s0Nz56aXu2a086F2AQAAAFASLP5eoH79+mrYsKF++OEHfy8F+FVKirRxo/tXpkePv7yOZ7crdNlSSVLmgMvzPJdw5GcdTj2kCqEV1a9u/xKJFQAAACWHmqf84stNAAAABBNqFwAAAAD+5tcd0f7q1KlTJbUU4BerV1vlcBiqW9elevXMnOshP6+S5WyyXFWqyNG+Y57nvt31jSTpioaDFW4LL7F4AQAAULKoecoXw5AWLMhQWFiUzpxJlcPhKu2QygybzaKYGPLqa+TV98ipf5BX/yCv/kFefc8XOS2PR3MWFbULAAAAAH/xeyPahg0btHv3bsXFxfl7KcCvfvzR/evSs6cj1/WcYzkvvUyyWnPdszvtmrNnpiTpmsbDSiBKAAAAlDRqnvLLMKSoKCkrS3I4zj8enrHZyKs/kFffI6f+QV79g7z6B3n1PXLqX9QuAAAAAPzN60a06dOnF3jPNE1lZWVp3759mjlzpgzDUK9evbxdCggIK1a4m8x69HD+edE0FbbI3YiWOWBgnmd+PBiv0xmnVSWiqnrU5ncAAAAgmFDzAAAAAAgG1C4AAAAAAoXXjWhPP/20DA/2tzZNU1WqVNE999zj7VJAqTt1ytD27e5GtEsu+bMRzbprp6z798kMDVVW7755nvt2l/sPAK5ufI1slhI7CRcAAAA+QM0DAAAAIBhQuwAAAAAIFF53xpxv62abzaaYmBi1b99et912m6pXr+7tUkCpW7PG3YTWtKlT1aqZOddDFy6QJNkv6SFFR+d6Jt2RrgX75kmSrml8XQlFCgAAAF+h5gEAAAAQDKhdAAAAAAQKrxvRli1b5ss4gIC2erW7Ea1bN2eu62HfL5QkZV42KM8z3+9fqFR7iupUqKtONTr7P0gAAAD4FDUPAAAAgGBA7QIAAAAgUFhKOwAgGKxa5W5Eu/jiPxvRjKQzsq1dI0nK6n9Znmeyj+Uc0vhaj7ZFBwAAAAAAAAAAAAAAAIKV1zuiFebYsWNavHixnE6nevTooUaNGvljGaBEJCdLW7e6ezb/2ogWsuIHGS6XHE2byVWnbq5nzmYma+nviyVJQ5twLCcAAEBZQ80DAAAAIBhQuwAAAAAoScXaES0xMVF33XWXXn311Zxr69ev1+WXX66XXnpJr776qgYPHqyPPvqo2IECpWXNGqtM01DDhi5Vr27mXA9d+r0kKavPpXmeWbBvnjKdmWoW01wtKrcssVgBAADgW9Q8AAAAAIIBtQsAAACAQOD1jmiHDh3SzTffrNTU1FzHDj7//PNKT09XdHS0atasqV27dun1119Xhw4d1LZtW1/EDJSoVavcvybdujn+vGiaCl22RJKU1TdvI9qMnV9Lkq5pMoxjOQEAAIIUNQ8AAACAYEDtAgAAACBQeL0j2qeffqqUlBS1bdtWDzzwgCT3Gzc7d+5USEiIZsyYoblz5+qZZ56RaZqaOnWqz4IGStLPP1slSd26/Xksp3XHdlmPHpEZESF7t0tyjT+edlwrDv0gSRrS5NqSCxQAAAA+Rc0DAAAAIBhQuwAAAAAIFF43oq1atUphYWEaP368LrroIknSjz/+KEnq2rWr6tWrJ0m68cYbFRMTo/Xr1/sgXKBkpaRImze7f00uvvjPRrSc3dAu6SGFh+d6Zu6emXKZLrWr1l4NL2hUcsECAADAp6h5AAAAAAQDahcAAAAAgcLrRrQjR46ofv36io2Nzbm2cuVKGYahbt265VwzDENxcXE6efJk8SIFSkFCglVOp6G6dV2qXdvMuR667HtJ+R/LOWv3t5Lcx3ICAAAgeFHzAAAAAAgG1C4AAAAAAoXXjWiGYeT698zMTG3cuFGS1Llz51z3zp07J5vN5u1SQKlZvTrvsZxGyjmFrFktSbL/rRHtWOpRJRz5WZI0uOGQkgkSAAAAfkHNAwAAACAYULsAAAAACBReN6LVrl1bBw4cUHp6uiTpp59+UlZWlipVqpSz9bMk7d27VwcPHlSdOnWKHy1QwlatchfkF1/syLkW8tMKGXa7nPUbyNmwca7x8/fNlSlTHap3Uq0KtUs0VgAAAPgWNQ8AAACAYEDtAgAAACBQeN2I1qlTJ6Wlpempp57S0qVL9frrr8swDPXv3z9nzLZt2/TQQw/JNE317NnTJwEDJSUtTdq0yf0r0rXrnzuiFXYs57w9syVJVza8ugQiBAAAgD9R8wAAAAAIBtQuAAAAAAKF1/sv33XXXVqwYIEWLlyohQsXyjRNRUVFadSoUZKkVatW6Y477pBpmoqLi9PIkSN9FjRQEtats8puN1Szpkv165vui6ap0GVLJOVtRDuRdkKrDv8kSbqy0VUlGisAAAB8j5oHAAAAQDCgdgEAAAAQKLzeEa169er66quvdPnll6tBgwbq06ePpk2bptq13ccR1qtXTxaLRX379tVXX32l2NhYnwUNlITVq62SpG7dnDIM9zXrnt2y/v6bzNBQZV2S+62x7/bNk8t0qU3VdqpXsX4JRwsAAABfo+YBAAAAEAyoXQAAAAAECq93RJOkOnXq6K233sr3Xq1atbRy5UpVqlSpOEsApSa7Ee3ii/Mey2nveokUFZVr/Ly92cdyshsaAABAWUHNAwAAACAYULsAAAAACATFakT7u+TkZKWmpioqKkoXXHABRQ2CVkaGtH59diOaI+d66FJ3I9rfj+U8nXFKKw7+IIljOQEAAMoyah7kYppSaqr7L4ertKMpO2wWKVTk1dfIq++RU/8gr/5BXv2DvPqeL3IaGamcIy7KMWoXAAAAAKWh2I1ohw4d0vvvv69ly5bp9OnTOdcrVqyoXr16afTo0apbt25xlwFK1MaNVmVmGqpa1aVGjUz3xfR0haxeKUnK6tc/1/hF+76T03SqReWL1KhSk5IOFwAAAH5EzYN8maYqDBwgJfysmNKOpYwir/5BXn2PnPoHefUP8uof5NX3ipNTe+euSpq7qFw2o1G7AAAAAChtluI8nJCQoGuuuUbTp0/XqVOnZJpmzl/JycmaO3euhg4dqlWrVvkqXqBErFrl3g2tWzdnzp9XhKz+SUZGhpy1asvZtFmu8XP3zJIkDW50dUmGCQAAAD+j5kGhyuGXmwAAAAhM1C4AAAAAAoHXO6KdOHFCY8aM0dmzZ9W0aVPdcsstatmypaKjo5WcnKytW7fq888/165du/TII49o7ty5qlq1qi9jB/zmr41o2UKXLZH0x7Gcf/nC6Wxmsn44GC9JurIhjWgAAABlBTUPCmUYOrdgsWLCDJ05kyoHx3H5jM1mUUxMFHn1MfLqe+TUP8irf5BX/yCvvueTnJbDozmpXQAAAAAECq8b0T7++GMlJyerb9+++t///qeQkJBc91u1aqVhw4bpgQce0PLly/XFF1/ogQce8DrQffv2afz48Vq/fr1OnTqlGjVqaODAgbr77rsVGRlZpLmOHTumCRMmaNWqVTp69KiqVKmivn376v7771dsbGy+z2zZskUTJkzQ1q1bdfbsWdWpU0dDhgzRiBEjcn32NWvW6NZbb/UojpdffllDhw7N+ffhw4dr48aNBY5/7LHHdMcdd3j4KeGtrCxp/Xp3I9rFF/+lEW3p9+77fS7NNX7R/u9kd9nVNKaZmsU2L7lAAQAA4FclWfNQ7wRpvWMYUlSUlCWJL599x2Yhr/5AXn2PnPoHefUP8uof5NX3yKlX+L4m//rlrxYsWKDJkydr7969cjqdat68uUaMGKEBAwbkGvfOO+/o3Xff9Sj2pUuXqnbt2pKkjIwMtW/fXk6ns8Dx06dPV6tWrTyaGwAAAAhWXjei/fDDD7LZbHrxxRcL/B/7kJAQvfjii+rVq5eWLFnidWHzyy+/aMSIEUpLS1Pr1q3VqlUrbdiwQe+//77i4+P1+eefKzo62qO5Dhw4oBtuuEEnTpxQ06ZN1adPH23fvl1Tp07VkiVL9NVXX6lGjRq5nomPj9fo0aPlcrnUsWNHVaxYUWvXrtVrr72mVatWaeLEiTk5qFKligYPHlzg+ocPH9b69esVEhKiJk2a5Fx3Op1KTExUaGioLrvssnyfbdy4sUefEcWzZYtFaWmGYmJMNWvm/sMOy4HfZduzW6bVKnvPXrnGz907W5J0JcdyAgAAlCklVfNQ77hR7wAAAADe4fua/OuXbK+99pomTZqkyMhIdenSRVlZWUpISNCYMWM0evRojRkzJmdss2bNCq15tmzZov379ysuLk4xMTE51xMTE+V0OhUXF6cOHTrk+2ylSpU8ygsAAAAQzLxuRDt8+LCaNm1a4Bsp2SpXrqymTZvqt99+82odh8OhRx55RGlpaRo3bpyGDRsmyf12ycMPP6xly5bpzTff1L/+9S+P5nviiSd04sQJ3X///TmFltPp1AsvvKAvv/xSzz77rCZOnJgzPjk5WWPHjpUkffjhh+revbskKSkpSaNGjdLKlSs1efLknDf3GzVqpNdffz3ftdPT03X99ddLkv71r3/levNl7969Sk9PV7t27Qp8HiUjIcG9G1rnzk5ZLO5roSt+kCQ52nWQWfGCnLEpWecU/7v7yE6O5QQAAChbSqLmod4BAAAAUFx8X5N//SJJq1ev1qRJk1SzZk1NmzZNtWrVkuRuHLvttts0fvx49erVS61bt5YkDRgwIM8uadn27NmjYcOGKSwsTO+9956ioqJy7m3btk2SNHjwYD3yyCMefX4AAACgLLJ4+6BhGLLb7R6Ntdvtcrm820Z7/vz5OnDggLp165ZT1EhSeHi4XnrpJUVGRurrr79WcnLyeedat26d1q1bp/r162v06NE5161Wq55++mnFxcVp+fLl2r17d869adOmKSUlRUOGDMkpaiT3mysvv/yyJOmTTz4pdLvlbC+88IJ27typyy67LOcLmmzZRcpFF1103nngX9mNaJ06/fnfNOTHeElSVs/eucYu+W2xMp2ZanBBQ7WszH87AACAsqQkah7qHQAAAADFxfc1Bdcv2Y1sDz/8cE4TmiQ1b95cDz30kEzT1EcffXTeeDMzMzVmzBilpaXp8ccfV/PmzXPdz655OHoTAAAA5Z3XjWj169fX3r17dejQoULHHTx4UHv27FG9evW8WmfZsmWSpP79++e5FxMToy5dushut2vFihUez9WvXz9ZLLk/ekhIiPr27StJWrp0aZ5n8nsDplGjRmratKlOnDihX375pdC1ExIS9O233+qCCy7Qs88+m+c+RUpgMM3cO6JlXwz90b0jmr1Xn1zjs4/lHNxwiAzDKLlAAQAA4HclUfNQ7wAAAAAoLr6vyb9+SUlJUUJCgqxWq/r165fnmQEDBsgwDC1fvvy8L998+OGH2rNnjzp06KAbb7wxz31qHgAAAMDN60a0fv36yel06v/+7/907ty5fMecO3dOY8eOlWma+f5Pvid27twpSWrWrFm+9xs3bizJvY2yp3M1bdrU47l27dpV5Gf+zuFw6N///rck6YEHHlDlypXzjNm+fbskKS0tTffcc4+6d++uNm3aaOjQoZo2bZrXbyihaPbvN3TihEWhoabatnUXntYd22U5eUJmZKTsHTrljM1wZGjpb99Lkq5oOLhU4gUAAID/lETNQ71DvQMAAAAUF9/X5P/Mnj175HQ6FRcXp+jo6DzjY2NjVblyZWVkZGj//v0FxnrgwAF98MEHslgs+te//pXnpfSsrCzt2bNHlSpV0urVqzV8+HB17NhRHTp00O23366VK1cWODcAAABQ1ti8ffDWW2/VV199pY0bN2rgwIG65ppr1LJlS1WoUEHnzp3Ttm3bNHPmTJ08eVLVqlXTiBEjvFrn+PHjkqTq1avne79atWq5xvlyruTkZGVkZBR7/blz52rnzp2qWbNmniNqJMnlcmnHjh2SpOeee04NGjRQu3btdOjQISUmJuqFF17QihUr9O6778pm8/o/GTyQvRta69YuhYe7r4X+cSynvevFUmhoztifDv2gNEeqakbFqW219iUeKwAAAPyrJGoe6h3qHQAAAKC4+L4m/2fOt0b2MydPntTx48fVqFGjfMdMmDBBmZmZGjRoUJ4jOSV345vdbldSUpKefPJJtW3bVl26dNHu3bu1atUqrVq1So8++qhGjRpVYBwAAABAWeH1n/JXrFhRkyZN0p133qkTJ05o0qRJecaYpqnq1avr/fffV8WKFb1aJy0tTZIUnt0V9DfZ17PHeTJXRESER3Nl/z00NDTP1tCerm+apj7++GNJ0h133KHQvzQyZdu/f79SU1Nls9k0btw4DRkyJOfe5s2bNXr0aMXHx2vixIm6//77z/cxC2Szeb0BntesVkuuvwe6devcvxJduzpz8hW2wn0sp6N331w5XPzbd5KkgQ0HKSTEWmIxBltOgwV59Q/y6nvk1D/Iq3+QV/8gryWnJGoe6h3qHeRFXv2DvPoeOfUP8uof5NU/yKvvkVPv8H1N/s+kpqYWGq8khYWFFRrzsWPHNHfuXBmGobvvvjvfMdk7QFerVk0TJkzIdTznzJkz9c9//lNvvvmm2rVrp06dOuU7hyeoecoO8up75NQ/yKt/kFffI6f+QV79ozzktVivmzdr1kwLFy7UtGnTFB8fr7179yo1NVVRUVFq0KCB+vbtqxtuuEEVKlTweg2r1SqXy5Vnq+O/M03To7kkeTxXdjFzvvGFrf/TTz9p586diomJyXd3AElq2LChVq1apXPnzql+/fq57rVp00bPPPOMxowZoylTpujee+8tsMgqjMViKCYmqsjP+UrFivkXk4Fm3Tr33/v1C1VMTKiUlSWt+kmSFHnVIEX+kUOX6dKi/e5GtOtaX1squQ2WnAYb8uof5NX3yKl/kFf/IK/+QV5Lhr9rHuod6h0UjLz6B3n1PXLqH+TVP8irf5BX3yOnRcf3NXmf8XSNvz7zd1OnTpXdblfv3r3z3Q1Nkq6//nr17NlTVqs1z+5r11xzjbZt26YpU6Zo8uTJXjeiUfOUTeTV98ipf5BX/yCvvkdO/YO8+kdZzmuxzz2JiorSqFGj/LalcFRUlJKSkpSenp7v/eytmAt6a+bvc0nyeK7s8ZmZmXK5XPl+IXK+9efOnStJGjRoUM6bNfmpXLmyKleunO+93r17y2q16syZMzp06JDq1KlT4DwFcblMnT17/reQfM1qtahixQidPZsup9NV4usXRVKStG2b+795ixapOnNGsq1eqQqpqXJVqaLk2g2lM+43qDYcW68jKUcUHRKtthd01pk/rpeEYMppMCGv/kFefY+c+gd59Q/y6h/BmteKFSOC9g0ff9Y81DvUO8iLvPoHefU9cuof5NU/yKt/kFffC9acBkq9w/c1+T+TfT0/mZmZkqTIyMh878+fP1+Su6GsIBaLRXFxcQXe79evn6ZMmaItW7YUOOZ8qHnKFvLqe+TUP8irf5BX3yOn/kFe/SNY81qUmqfYjWh/lZaWpnPnzikmJibfI1m8Ua1aNSUlJenEiRP5fiFx/PjxnHGezLVt2zadOHEi3/t/nys6OlrR0dFKSUnRiRMn8rzJcr717Xa74uPjJUlXXnnleeMrSGhoqGJjY3XixAmPtrQuiMNRej/ETqerVNf3xM8/u9+OatjQpZgYlxwOKXTZMklSVo9ecrgkudyfYf5u9xdufepeKqtCSuWzBUNOgxF59Q/y6nvk1D/Iq3+QV/8gr6XD1zUP9Q71DgpGXv2DvPoeOfUP8uof5NU/yKvvkdPi4/sa5YwpaI3zxfzLL7/o0KFDioqKUp8+fc77mQpSo0YNSQU33XmKmqfsIa++R079g7z6B3n1PXLqH+TVP8pyXov9is6hQ4f03HPPqUePHurQoYN69+6tNm3a6LLLLtNbb72lpKSkYs3frFkzSdLu3bvzvZ99PXucr+dq2rSp1+uvX79eZ8+eVfXq1dWuXbsC41q4cKEeffRRffbZZ/nez8jIUFJSkgzDyClY4HsJCe5GtM6dnTnXQn9cLkmy98xdZC7ct0CSdHn9QSUTHAAAAEqNP2se6h3qHQAAAMBX+L4m9zONGzeW1WrVoUOH8m0CO336tE6dOqWwsDDVq1cvz/3ly5dLkvr06VPoDtATJkzQAw88oNWrV+d7/+jRo5JEvQMAAIByoViNaAsWLNAVV1yhr776SidOnJBpmjl//fbbb/rggw90xRVXaN26dV6v0atXL0nSokWL8tw7c+aM1qxZo5CQEF1yySUez/X999/L5crdWWi327V06VJJyvVmS2Hr79mzRzt37lRsbKzatGmT5/7mzZslSR07dpRhGAXGde7cOc2bN0+TJ0+Ww+HIc3/evHmy2+1q27atLrjggvN9THjp741oxrmzsm1w/+xm9eydM25/8j7tOL1dVsOqS+sNKPE4AQAAUHL8XfNQ71DvAAAAAL7A9zV565ewsDB17dpVdrtdy/44/eSvFi1aJNM01b17d4WEhOS5n13zdOrUqdDPsm/fPi1atEgzZ87M93729d69exc6DwAAAFAWeN2ItmPHDj322GPKyMhQ27Zt9corr2jWrFlavHixZsyYoRdffFEtW7bUqVOnNGbMGB07dsyrdfr376+4uDj99NNPmjZtWs71jIwM/fOf/1RaWpqGDRumKlWq5Nyz2+3as2eP9uzZI7vdnnO9ffv2atWqlfbs2aM333xTpmlKkpxOp8aNG6cjR46oZ8+euvDCC3OeGTp0qKKjozVjxoycwkeSkpKS9NRTT0mSRo4cmW+RsmXLFklS27ZtC/2MAwcOVGxsrA4ePKiXXnopV8xr167VK6+8IsMw9OCDD3qSMnjBbpc2bszdiBayeqUMp1PO+g3kqlM3Z+yi/e7d0LrWvFgx4bElHywAAABKREnUPNQ71DsAAABAcfF9TcH1y6233ipJevXVV7V///6c64mJifrf//4nSbr77rvz/bxbt26VdP6a58Ybb5RhGJozZ47mzJmT697kyZM1e/ZsVapUKScWAAAAoCwzzOz/uy+iRx55RAsWLNDw4cP13HPP5TvGNE099dRTmjlzpkaMGKEnn3zSqyDXrFmjUaNGKSMjQy1btlTt2rW1ceNGHT9+XC1atNCUKVMUHR2dM/7gwYPq16+fJGnp0qWqXbt2zr1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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load the consumption and state evolution process \n", "gc_tp1 = res_015['gc_tp1']\n", "X1_tp1 = res_015['X1_tp1']\n", "X2_tp1 = res_015['X2_tp1']\n", "\n", "## Calculate exposure elasticity for consumption growth\n", "expo_elas_shock_0_015 = [exposure_elasticity(gc_tp1, X1_tp1, X2_tp1, T, shock=0, percentile=p) for p in quantile] # The first shock is the growth shock\n", "expo_elas_shock_1_015 = [exposure_elasticity(gc_tp1, X1_tp1, X2_tp1, T, shock=1, percentile=p) for p in quantile] # The second shock is the volatility shock\n", "expo_elas_shock_2_015 = [exposure_elasticity(gc_tp1, X1_tp1, X2_tp1, T, shock=2, percentile=p) for p in quantile] # The third shock is the consumption shock\n", "\n", "## Plot the exposure elasticity for consumption growth\n", "index = ['T','0.25 quantile','0.5 quantile','0.75 quantile']\n", "fig, axes = plt.subplots(1,3, figsize = (25,8))\n", "expo_elas_shock_0 = pd.DataFrame([np.arange(T),expo_elas_shock_0_015[0].flatten(),expo_elas_shock_0_015[1].flatten(),expo_elas_shock_0_015[2].flatten()], index = index).T\n", "expo_elas_shock_1 = pd.DataFrame([np.arange(T),expo_elas_shock_1_015[0].flatten(),expo_elas_shock_1_015[1].flatten(),expo_elas_shock_1_015[2].flatten()], index = index).T\n", "expo_elas_shock_2 = pd.DataFrame([np.arange(T),expo_elas_shock_2_015[0].flatten(),expo_elas_shock_2_015[1].flatten(),expo_elas_shock_2_015[2].flatten()], index = index).T\n", "\n", "n_qt = len(quantile)\n", "plot_elas = [expo_elas_shock_0, expo_elas_shock_2, expo_elas_shock_1] # For illustration purpose, the consumption shock is plotted in the second column, the volatility shock is plotted in the third column\n", "shock_name = ['growth shock', 'consumption shock', 'volatility shock']\n", "qt = ['0.25 quantile','0.5 quantile','0.75 quantile']\n", "colors = ['green','red','blue']\n", "\n", "for i in range(len(plot_elas)):\n", " for j in range(n_qt):\n", " sns.lineplot(data = plot_elas[i], x = 'T', y = qt[j], ax=axes[i], color = colors[j], label = qt[j])\n", " axes[i].set_xlabel('')\n", " axes[i].set_ylabel('Exposure elasticity')\n", " axes[i].set_title('With respect to the ' + shock_name[i])\n", "axes[2].set_ylim([0,0.00185])\n", "axes[1].set_ylim([0,0.0185])\n", "axes[0].set_ylim([0,0.0185])\n", "fig.suptitle('Exposure elasticity for the consumption growth')\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "6115a700", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Growth ShockConsumption ShockVolatility Shock
0.25 quantile0.0150.007180.000458
0.5 quantile0.01630.00780.000477
0.75 quantile0.01760.008420.000496
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" ], "text/plain": [ " Growth Shock Consumption Shock Volatility Shock\n", "0.25 quantile 0.015 0.00718 0.000458\n", "0.5 quantile 0.0163 0.0078 0.000477\n", "0.75 quantile 0.0176 0.00842 0.000496" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Calculate exposure elasticity for consumption growth at the limit\n", "log_f = LinQuadVar({'c': np.zeros((1,1))},gc_tp1.shape)\n", "Qf_components_log_C, _, η_C, η_C_series = Q_mapping(gc_tp1, log_f, X1_tp1, X2_tp1, tol = 1e-7)\n", "expo_elas_shock_0_015_limit = [exposure_elasticity(gc_tp1, X1_tp1, X2_tp1, len(η_C_series), shock=0, percentile=p)[-1].item() for p in quantile]\n", "expo_elas_shock_1_015_limit = [exposure_elasticity(gc_tp1, X1_tp1, X2_tp1, len(η_C_series), shock=1, percentile=p)[-1].item() for p in quantile]\n", "expo_elas_shock_2_015_limit = [exposure_elasticity(gc_tp1, X1_tp1, X2_tp1, len(η_C_series), shock=2, percentile=p)[-1].item() for p in quantile]\n", "expo_limit = pd.DataFrame([expo_elas_shock_0_015_limit, expo_elas_shock_2_015_limit,expo_elas_shock_1_015_limit],\\\n", " columns = ['0.25 quantile','0.5 quantile','0.75 quantile'],\\\n", " index = ['Growth Shock','Consumption Shock','Volatility Shock']).T\n", "expo_limit" ] }, { "cell_type": "markdown", "id": "c6038a5c", "metadata": {}, "source": [ "## 3.2 Calculate Price Elasticity for Consumption Growth\n", "\n", "Similarly, to calculate the price elasticity for consumption growth using the *price\\_elasticity* defined above, we need additional inputs\n", "- Log Stochastic Discount Factor\n", "\n", "We construct the log SDF using equation (7), then input it into the function to calculate price elasticity. The code uses the following formula for the log SDF:\n", "\n", "$$\n", "\\log S_{t+1}-\\log S_t=\\log \\beta-\\rho\\left(\\widehat{C}_{t+1}-\\widehat{C}_t\\right)+(\\rho-1)\\left(\\widehat{V}_{t+1}-\\widehat{R}_t\\right)+\\log N_{t+1}^*\\tag{7}\n", "$$\n", " \n", "where \n", "\n", "$$\n", "\\log N_{t+1}^*= (1-\\gamma)\\left(\\widehat{V}_{t+1}-\\widehat{R}_t\\right)\n", "$$\n", "\n", "and $\\left(\\widehat{V}_{t+1}-\\widehat{R}_t\\right)$ is the logarithm of the risk adjustment continuation value. The random variable $N_{t+1}^*$ is positive and has expectation one conditioned on date $t$ information. It implies a substantively interesting change in the probablity measure.\n", "\n", "We approximate the term $\\frac{1}{1-\\gamma}\\log N_{t+1}^*$ differently than the $\\left(\\widehat{V}_{t+1}-\\widehat{R}_t\\right)$ in the $(\\rho-1)\\left(\\widehat{V}_{t+1}-\\widehat{R}_t\\right)$ term. We use an approximation of the former term that ensures $N_{t+1}^*$ is positive and has conditional expectation one. " ] }, { "cell_type": "code", "execution_count": 11, "id": "c014867e", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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Wrl2rCRMmKDc3V71791avXr20cuVKvfrqq5o/f77ef/99xcbGVnu/ubm5uvPOO3Xw4MHjjtu40X+axV69eqljx45l1kdElL2W73PPPadp06YpOjpagwYNUmFhoZYtW6Y777xTd9xxh+68885qzxfHZ5rS4sV2zZjh1Jw5DuXlHe1SPeMMjy691KOxY92KiwvhJAEAANDokW8AAEBNnHbamfrmm/9pwYJ5uuCCcaXWZWVlatWq5XI6nRo4cFCV9+n1erVpk/93hFNO6VvhuIiICC1b9pNSUw9o4cLvdf75F5Za73a79d13X0uSzjjjzOMeMzGxmTp16qxt21K0ZMkinXXW0FLr161bo6yssh+8FF+SJz09vcy6devWlrNstSRpwIBBZT6kkaSffvpRUvmXrqmOmp5doEePkxQZGandu3/Vr7/uUvv2HUqtN01T99xzuwoK8nXLLXfolFP61eh4taVv3/6SpMWLFyo/P1+RkZGl1h88mKo1a1bJZrOpX7/+oZgiLEa+AQAANUG+Id+Qb8JPvW9E83g8uu+++5Sbm6tJkyZp/PjxkqT8/Hzde++9mjdvnqZMmaLHH3+8WvvdvXu37r33Xq1bt67SsevXr5ck3XPPPRo8eHCl45csWaJp06apVatWmjFjhtq08Z9CctOmTbruuuv08ssva8iQIerdu3e15ozyHTxo6MMPHXrvPZe2bz96SsVOnXy67DK3LrnErfbta/YPLQAAAFAbyDcAAKCmhgwZquTkllq2bIk+/vgjXXzxpZKkgoJ8TZ78tPLy8nTRReNLXX7G4/EEvrXdpk3bMt/O37Fju/Lz8xUX11Tt23c87vHHjbtUr732kv7973+qe/ee6ty5S4nj/0V79vymU07pp4EDT6v0tVx99XX685//pBdffE4dOnQMHPvQoYP6618nlbtN585dtXjxQn3++X/1u9+NVXS0/5IHmzdv0vTpb5YZX3yJk/Xr1yojI10JCYmS/Gcc+Pjjmfrii0+L5l9Y6XyPx+VyBR5nZ2crrprfho2MjNS4cZfqP/95V3/+85/09NOTA5cm8nq9mjr1Fa1YsUxNmsSpW7cege0yMzOVlZWpiIhItWx5/LM0WKFPn77q2fNkbdy4Xn/5y+N69NE/Bz5My8jI0OOPPyS3260RI0apefMWdT4/WIt8AwAAaop8Q74h34Sfet+I9uWXX2r37t06/fTTAyFG8r9xn3nmGQ0fPlwzZ87U3XffraZNm1a6v/z8fE2fPl2vvfaajhw5onbt2mn37t3H3ab4GzUnn3xyleZcfIroe++9NxBiJKl79+6655579MQTT+iNN97QP/7xjyrtD2X5fNKiRXa9+67/7Gdut78jNzbW1MUXu3XllW717etTHV7mGAAAAKgU+QYAANRURESkHn30Sf3f/92tKVP+qi+++FStW7fR+vVrdejQQXXt2k233npHqW0OHkzVVVf5f/f46KPP1KpV61Lriy87UvzBwPFcccXVWr9+jRYvXqgbbrhavXr1UdOmTbV27RplZKSrQ4eO+vOfn6nSaxk16lxt2LBes2Z9oAkTrlDfvgPkcjm1YsXyCn8XuvDCi/Xf/87S9u3b9Pvfj1OvXn2UlZWpdevW6PTTz9SGDetUUFAQGD98+Ci9/fYb2r9/n6644mL17t1XNpuhzZs36eDBVJ1wwonauXOHMjMz5PV6ZbfbqzT3Y7lcLiUlJSs19YDuuOMmtW/fQY888mS1Lkt40023KiVli37+eamuvvoyde/eQ/HxCdq8+RcdOLBfERERmjTpb4EPQiRp9uwP9dZbr+uUU/rppZemBjX3mvrzn5/R3XffqgUL5mnVqhXq3buvvF6PVq1aqby8XJ18cm/df/8jIZkbrEW+AQAANUW+Id+Qb8JPvW9EmzfPfw3bUaNGlVmXkJCgQYMGaf78+Vq4cKHOP//8Svc3Z84cvfDCC4qPj9ekSZNks9n08MMPVzg+Oztbu3fvVvv27QPdpcdz5MgRLVu2THa7XSNGjCizfvTo0XryySe1YMGCGv3gN1ZZWdL77zv11lsu7dx59Oxn/fp5dc01bl14oVtBnOUbANAYmGb5t5Lr7IYUYUg5OZLbK0MVjAvcylkmld2ueNvKHld1XBX3ZaiicVU4Xi3PRaYpu8MmxUXJnpUreX3V3j74x6V3VdG4CutVkfo0pmi93W5ITaLkOJwnw3ucGla0m4hIeQYMlBz1PiY0WOQbAABQG/r1G6DXX39Hb775ulavXqGdO3eodevWOv/8C3XFFVcrJqZ6fyDLzMyQJCUlJVc61uFw6NlnX9Dnn3+iL7/8TJs3/yKfz6fWrdto3LjxuuKKa0p9kFCZe+65X7169dGsWf/R2rWr5HJF6Oyzh+rWW+/URReNKTM+KSlZU6e+rTffnKqff16qn35arNat2+iWW+7UZZddoYsv/l2p8dHRMXrttbf01lvT9PPPP2n58qWKi2uqli1b6bLLrtQll1ymiROvU0rKFq1Y8bNOPbXyMx1U5Ikn/qIXX3xOu3bt0KFDh7Rnz2/q0qVrlbd3uVx6/vl/6ssvP9NXX32pbdu2yu32KDk5WWPHjtOVV16jdu3aBz0/q7Rq1VpvvPGeZs58XwsWzNXSpUvkcjnVqVNnnXPOeRo79qIyZ6lAeCDfAACA2kC+Id/UJ+SbmjPMml4c1mJjxozR9u3bNWPGDA0YMKDM+ueff16vv/66brrpJt1///2V7m/evHlat26drr/+esXFxenjjz/Www8/rKFDhwa+CVPSkiVLdN1112nw4MHq16+fvvrqK/3666+KjY3V6aefrttuu00nnnhiYPyaNWt02WWXqV27dvruu+/KncOZZ56pQ4cOac6cOerUqVM1quHn9fqUnp5T7e1qg8NhU0JCjDIycuTx+OrsuCkphl5/3aUPP3QqN9d/mrMmTUyNH+/W1Ve71atX3c3FSqGqb2NCja1VL+trmpLHI7ndMrweyev1N8B4vTJ83qLn3hLPfcc8L7p5jnnu88oosa+jz4vX+4557pXh8VQ83vTJ8BU1Lvl8R2+mL7DMJlORTrsK8grk8x5dbpQab/q38fkkn3l0XanlRdsEjnXMupJzKF5uqvSxzBL7LbVPSSWbsCps4DL9xzHNo+PLjCu7nRH4taWCprJyjnt0GwBVkXP/Q8p9oO6/zZOYGCO73Vb5wAaOfFNWY8w3jQk1thb1tRb1tV5xjQ8cSFdq6l41a9ZKTqer8g1RZQ6HrUG/fwcP9v++9MUX31WpySQU6kON//Of97Rq1XL97W9/D+k8rFAf6lvM7S5UWtq+Kv9b1RgyDvmmfGSc8EV9rUV9rUeNrVWyvnl5+dX6vQFVV59+P6wu8k3VkG/qTnUyTnXyTb1v00tNTZUkJSeX362alJRUalxlhg8fruHDh1f5+Bs2bJAkLVq0SD///LMGDhyoVq1aacOGDfr88881d+5cvfLKKzr99NOrNN/iOR86dEipqalBB5nGwOeTFiywa+pUl+bNO/pW7dHDqxtvdOvii92KiQnhBIHGxOuVCgpkuAulgsKi+wIZhaUf270eyWXImZYle36BVFgoo7BQKiyQUbydxyN53DLcnqOPPf7Hhttder23qIGs6LF/vdvfFBZ47PFv7y7eT9F6j7uo+cwb6urVuohQTwDHZRZfF9owjt6K1dZjlb72tFmj/aoKYyqaxzE7KLGuvDkZhmSz2eQzTX+vYK3V49h5qNxxNapTRerVGEMyJIfdJo+3uDG0evswIyPkPvOsyueCoJFvAAAAUNuWLv1RJ53UK9TTQCNEvgEAAEBtI980fPW+ES03N1eSFBkZWe764uXF42rbxo0bJUkDBw7U3//+dzVv3lySVFhYqMmTJ2vGjBm655579O233youLk45OTnHna8kRURE1HjODkdovklV3OFo5Te5CgqkDz5w6JVXnNq61X8cwzB17rle3XyzW2ed5Sv6DDX8vk1WF/Vt7Bp0jT0eKS9PRn6ejPx8KT/ff1+0TPn5MvLyji4ryC+9LD9PRl7xfZ6MgoKjywoLihrGCv3LixvOCouayarZzNVQrpBr2u1SiZtpK35s89/b7EfHOBySzb+81HZF25jF25RaVvzcFtiHWWpMif3Z7MVdOqVupmFINkMybLI57IqIilB+oUem/GNNm62o2ankdkapffnH2Mrdvwzj6D5KLCs1h6oco3j/Fd2kCteZhuRvoDn+uOPtz99gdLxxlexX/iYlu92uuKbRyj6cL6/PrNbrqFIjUSNnt9sUFxelI9l58nrrzzc+wklxjXNrWON6HxIaMPJN+cI53zR21Nha1Nda1Nd61NhaJSMMJ4q2Rn2o8aeffqyMjAz9/vdXhmYCFqoP9S2P3W6E7PfX+oZ8UzEyTniivtaivtajxtYqWV+7nc8LrFBffz8MF/WhvuSb0KjtjFPvP2Oy2+3y+XwyKjt7g0X/pSZPnqw77rhDSUlJio092lrhcrn06KOPauXKlfrll1/06aef6pprrpHdbpekSudbkznbbIYSEkJ7KrC4uKpfB7mqDh+WXntNmjJF2rfPv6xJE+mGG6Q77jDUqZNDDeAtWyusqC9Ks7TGbreUleV/U+fkSEeOVO2+sjEej3Vzri6XS4qIKHtf2TKns/TN4Sj/8fHWVfdxyecOR4lmMZuO/Ze6ocSCiv9UhtoS16RJqKcQ1vj/nPWocf1FvikrXPMNSqPG1qK+1qK+1ouNjZTNZqO5wyLh8EGjw1G/3xuhrPHYsRfowgsvlMPhDNkcrFZf3sM+nyGbzaamTaOP28jUmJBvykfGCX/U11rU13rU2FpxcVFyuQwdOkTGsUp9+f0wWOSbipFv6pZVGafed/XExMQoMzNTeXl55a7Pz8+XJEVFWfM/TJfLpRNPPLHcdXa7XUOHDtUvv/yidevWBeZbcl7lKSgokCRFR0cHNSefz1R2tjXfIKpM8Vk2smvxTCaHDklTpzo1bZpTmZn+ANiqlU+33+7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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def calc_SDF(res):\n", " r'''\n", " This function loads external results for the long-run risk model with different rho values, and then calculates the SDF.\n", "\n", " Parameters\n", " ----------\n", " res : dictionary of LinQuadVar\n", " Stores the external solution for the long-run risk model\n", "\n", " Returns\n", " -------\n", " log_SDF : LinQuadVar\n", " Stores coefficients of the log SDF\n", " gc_tp1 : LinQuadVar\n", " Stores the coefficients of the consumption growth process.\n", " X1_tp1 : LinQuadVar\n", " Stores the coefficients of the first order expansion of the state evolution equations.\n", " X2_tp1 : LinQuadVar\n", " Stores the coefficients of the second order expansion of the state evolution equations.\n", " '''\n", " n_J, n_X, n_W = res['var_shape']\n", " β = res['β']\n", " ρ = res['ρ']\n", "\n", " X1_tp1 = res['X1_tp1']\n", " X2_tp1 = res['X2_tp1']\n", "\n", " gc_tp1 = res['gc_tp1']\n", " gc0_tp1 = res['gc0_tp1']\n", " gc1_tp1 = res['gc1_tp1']\n", " gc2_tp1 = res['gc2_tp1']\n", "\n", " vmr1_tp1 = res['vmr1_tp1']\n", " vmr2_tp1 = res['vmr2_tp1']\n", " log_N_tilde = res['log_N_tilde']\n", "\n", " S0_tp1 = LinQuadVar({'c':np.log(β) - ρ*np.array([[gc0_tp1]])}, shape = (1,n_X,n_W))\n", " S1_tp1 = (ρ-1)*vmr1_tp1 -ρ*gc1_tp1\n", " S2_tp1 = (ρ-1)*vmr2_tp1 -ρ*gc2_tp1\n", "\n", " log_SDF = S0_tp1 + S1_tp1 + 0.5 *S2_tp1 + log_N_tilde\n", "\n", " return log_SDF, gc_tp1, X1_tp1, X2_tp1\n", "\n", "## Calculate the log SDF when ρ = 2/3\n", "log_SDF, gc_tp1, X1_tp1, X2_tp1 = calc_SDF(res_006)\n", "\n", "## Calculate price elasticity for consumption growth when ρ = 2/3\n", "price_elas_shock_0_006 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=0, percentile=p) for p in quantile]\n", "price_elas_shock_1_006 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=1, percentile=p) for p in quantile]\n", "price_elas_shock_2_006 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=2, percentile=p) for p in quantile]\n", "\n", "## Calculate the log SDF when ρ = 1\n", "log_SDF, gc_tp1, X1_tp1, X2_tp1 = calc_SDF(res_010)\n", "\n", "## Calculate price elasticity for consumption growth when ρ = 1\n", "price_elas_shock_0_010 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=0, percentile=p) for p in quantile]\n", "price_elas_shock_1_010 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=1, percentile=p) for p in quantile]\n", "price_elas_shock_2_010 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=2, percentile=p) for p in quantile]\n", "\n", "## Calculate the log SDF when ρ = 1.5\n", "log_SDF, gc_tp1, X1_tp1, X2_tp1 = calc_SDF(res_015)\n", "\n", "## Calculate price elasticity for consumption growth when ρ = 1.5\n", "price_elas_shock_0_015 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=0, percentile=p) for p in quantile]\n", "price_elas_shock_1_015 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=1, percentile=p) for p in quantile]\n", "price_elas_shock_2_015 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=2, percentile=p) for p in quantile]\n", "\n", "## Calculate the log SDF when ρ = 10\n", "log_SDF, gc_tp1, X1_tp1, X2_tp1 = calc_SDF(res_100)\n", "\n", "## Calculate price elasticity for consumption growth when ρ = 10\n", "price_elas_shock_0_100 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=0, percentile=p) for p in quantile]\n", "price_elas_shock_1_100 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=1, percentile=p) for p in quantile]\n", "price_elas_shock_2_100 = [price_elasticity(gc_tp1, log_SDF, X1_tp1, X2_tp1, T, shock=2, percentile=p) for p in quantile]\n", "\n", "## Plot the price elasticity for consumption growth\n", "fig, axes = plt.subplots(4,3, figsize = (25,26))\n", "index = ['T','rho 006 0.25 quantile','rho 006 0.5 quantile','rho 006 0.75 quantile','rho 010 0.25 quantile','rho 010 0.5 quantile','rho 010 0.75 quantile',\\\n", " 'rho 015 0.25 quantile','rho 015 0.5 quantile','rho 015 0.75 quantile','rho 100 0.25 quantile','rho 100 0.5 quantile','rho 100 0.75 quantile']\n", "price_elas_shock_0 = pd.DataFrame([np.arange(T),price_elas_shock_0_006[0].flatten(),price_elas_shock_0_006[1].flatten(),price_elas_shock_0_006[2].flatten(),\\\n", " price_elas_shock_0_010[0].flatten(),price_elas_shock_0_010[1].flatten(),price_elas_shock_0_010[2].flatten(),\\\n", " price_elas_shock_0_015[0].flatten(),price_elas_shock_0_015[1].flatten(),price_elas_shock_0_015[2].flatten(),\\\n", " price_elas_shock_0_100[0].flatten(),price_elas_shock_0_100[1].flatten(),price_elas_shock_0_100[2].flatten()],index = index).T\n", "price_elas_shock_1 = pd.DataFrame([np.arange(T),-price_elas_shock_1_006[0].flatten(),-price_elas_shock_1_006[1].flatten(),-price_elas_shock_1_006[2].flatten(),\\\n", " -price_elas_shock_1_010[0].flatten(),-price_elas_shock_1_010[1].flatten(),-price_elas_shock_1_010[2].flatten(),\\\n", " -price_elas_shock_1_015[0].flatten(),-price_elas_shock_1_015[1].flatten(),-price_elas_shock_1_015[2].flatten(),\\\n", " -price_elas_shock_1_100[0].flatten(),-price_elas_shock_1_100[1].flatten(),-price_elas_shock_1_100[2].flatten()],index = index).T\n", "price_elas_shock_2 = pd.DataFrame([np.arange(T),price_elas_shock_2_006[0].flatten(),price_elas_shock_2_006[1].flatten(),price_elas_shock_2_006[2].flatten(),\\\n", " price_elas_shock_2_010[0].flatten(),price_elas_shock_2_010[1].flatten(),price_elas_shock_2_010[2].flatten(),\\\n", " price_elas_shock_2_015[0].flatten(),price_elas_shock_2_015[1].flatten(),price_elas_shock_2_015[2].flatten(),\\\n", " price_elas_shock_2_100[0].flatten(),price_elas_shock_2_100[1].flatten(),price_elas_shock_2_100[2].flatten()],index = index).T\n", "n_rho = 4\n", "n_qt = len(quantile)\n", "plot_elas = [price_elas_shock_0,price_elas_shock_2,price_elas_shock_1]\n", "shock_name = ['growth shock', 'consumption shock', 'volatility shock']\n", "rho_y = ['rho 006', 'rho 010', 'rho 015', 'rho 100']\n", "rho_label = ['rho = 2/3','rho = 1','rho = 1.5','rho = 10']\n", "qt = ['0.25 quantile','0.5 quantile','0.75 quantile']\n", "colors = ['green','red','blue']\n", "\n", "for k in range(len(plot_elas)):\n", " for i in range(n_rho):\n", " for j in range(n_qt):\n", " sns.lineplot(data = plot_elas[k], x = 'T', y = rho_y[i] +' ' + qt[j], ax=axes[i,k], color = colors[j], label = qt[j]+', '+rho_label[i])\n", " axes[i,k].set_xlabel('')\n", " axes[i,k].set_ylabel('Price elasticity')\n", " axes[i,k].set_ylim([0,0.185])\n", " axes[i,k].set_title('With respect to the '+ shock_name[k])\n", "\n", "fig.suptitle('Price elasticity for the consumption growth')\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "938eefcd", "metadata": {}, "source": [ "## 3.3 Shock Elasticities under Different Parameter Settings\n", "\n", "Below is a brief UI to select parameters shown in the long-run risk model [1]. The stochastic volatility process has been normalized with its mean equal to 1. By changing the inputs of parameters, we can see how shock elasticities vary with respect to these parameters." ] }, { "cell_type": "code", "execution_count": 12, "id": "6d4e2287", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 1: error = 9.93379444\n", "Iteration 2: error = 0\n", "Current paramter settings\n", "γ = 10\n", "β = 0.998\n", "ρ = 0.6667\n", "α = 0.979\n", "ϕ_e = 0.0002\n", "ν_1 = 0.987\n", "σ_w = 0.03\n", "μ = 0.0015\n", "ϕ_c = 0.0078\n" ] }, { "data": { "image/png": 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DBw/WjBkz9OGHH5Y6F4cMGaJ9+/YF9znttNMkSYsWLVJ8fHyx+bZs2aIZM2ZoxYoVSk9PV/PmzTVw4EDdcsstat68ebnPoaTjfc8UvHMgNTVVb775ppYuXap9+/bJZrPplFNO0cUXX6xrr71WLper1Nzbt2/XrFmztG7dOu3bt092u11t27bVwIEDdcMNN6hp06bFxhd83VatWqXFixfrrbfe0u+//65GjRrp9NNP180336y+ffuW+Tzy8vI0d+5cffHFF9q2bZs8Ho9atWqlQYMGady4ceW+yysjI0Nz587Vl19+qd27dysvLy/4PfyXv/xFUVFRkqr2tSmqsvstX75c7777rtauXau0tDRFRUXp9NNP15/+9Kfg91JllPe9sHnz5lJj16xZo9dff11r165Vdna2WrVqpeHDh2vChAnB513St99+qwULFmj9+vU6duyYYmNj1bdvX918883B51YVaWlpmj17tr766ivt27dP4eHhOvvss3Xrrbfqs88+0/Tp03XrrbfqtttukxS45P8NN9ygXr16aerUqXrooYf022+/KSoqSldccYXuv//+4NwLFy7Ue++9p99++00ZGRmKiYlR7969dc011xT7eb5gwQJNnjxZHTp00MKFC0utcfjw4dq1a5dGjBihF198sVjf0aNHNWDAAJmmqfnz5+vqq68O9v3888/q2rWr2rRpo8WLF5ead8mSJXrrrbf022+/yev1qm3btrrkkkt00003KSwsrNIx9Pl8mj9/vr788kvt2LFDaWlpiomJUa9evTRq1KhSP4uKOnz4sF577TUtWrRIBw4cUOPGjXXm/2fvvsObKv83jt9tulugrLKHjDBkyAZZiiCKqCxRHICCCIigOAEFARFwAKIiyPyKoAjIKIqo7DJl75YCLZuy2gKlO78/+ktsbDrSNrSE9+u6vNSc9eSTc9Jz5zznOQ0a6NVXX1Xt2rVtLnPq1CnNmzdPW7du1YULF+Tp6anKlSurQ4cOeu6559L8HTE7duyYFixYoG3btunSpUvy8vJSzZo11aNHDz322GNZeq/79+/Xyy+/rFu3bunJJ5/UxIkTZTAYsrQsAGSEbGE/sgXZwhayBdkiL7OFPce+mb3ntubvno8//lgtW7bUt99+qy1btujatWsqUqSIWrZsqQEDBtj8HDdv3qyFCxfq+PHjunTpknx8fGQ0GvXYY4/pmWeescoA5twjpTxCyM0t7c/H5s/D/L0oyfJd8OSTT+rjjz/Wd999pz/++EMREREqUqSIHnnkEQ0dOlR+fn46duyYpk2bpp07d+rmzZsqV66cnn32WfXq1UsuLi6W7Zi/J3v27Km+fftq0qRJ2rx5s27duqUyZcqoQ4cO6tWrl9Xf0px8hyclJenXX3/VypUrdezYMd2+fVvFihVTo0aN1Lt37zT7xtmzZ/XII4+oWLFiCgoK0pIlS/TLL78oNDRUkmQ0GtW9e3d16dLF6n1lJioqSnPmzNHmzZt19uxZxcXFKSAgQI0bN1bPnj2tjo//ys53anaP23Xr1mnx4sU6fPiwrl27Jn9/fzVs2FB9+/bN8qhECxYs0JgxY+Ti4qKPPvpIL7zwQpaWAwCJLJUdZCmylC1kKbLUnc5SZ86cUbt27WQymbRs2TLVrFnT5nzt27dXWFiYPvnkE6vrH9m9TmCLPd+LqbOSJAUGBiowMFCNGzfW/PnzLflAkv788880j+78L/O+YX5/5u8Es+nTp2v69Onq3LmznnjiCfXt21dubm7atGlTmmNHSvm+bt68uW7evKlffvlFdevWzfT925MVU0tMTNSCBQu0fPlynTx5Uh4eHqpWrZqef/55dejQweYyUVFRmj9/vv7++2+Fh4crOTlZpUuX1kMPPaSXX3453WP08uXLWrhwof7++2+dPXtWJpNJ9913n5544gn17NkzS9e0rl27pp49e+r48eMyGo2aN2+ezRoi99ARDciG8PBwy3+XLFkyzfTp06drz549Kl++vAoUKKCbN2+qYsWKGd6VM2nSJH3//fcymUzy9/dXtWrVdOHCBa1fv17r16/XmDFj9Oyzz1rmP3HihF599VWrEzcvLy+Fhobq22+/1fLlyzVz5ky7hgB1lH379unVV19VdHS0PD09Vb58ebm5uens2bPau3ev9u7dq7Vr1+qHH36wXNSPj49X7969tXfvXhkMBlWoUEGlSpXSmTNntGrVKv32228aO3as5cTDaDQqJiZGhw4dkpRyIufp6ZnpsMa3bt3S4MGDFRQUJCnlmd1Go1GnTp3Sr7/+qj/++EM//vhjhidP9evX17Vr1xQWFiZ3d3dLBwqDwaDOnTtr+vTpWrFihc2AExsbqz/++EOS1LVr1wzb2qZNG+3du1dBQUFpAs7WrVst/719+/Y0AWfDhg2SZDkBsqVWrVry9fVVSEiI5X1JShMetm7dqjlz5ig5OVmVKlWSt7e3zp49q4ULF+qvv/7S8uXLVaxYsQzfy3+ld8xIKSfCAwcOVGRkpNzd3VWxYkWZTCYdPnxYhw4d0ooVKzRr1iyrYLV371698soriomJUcGCBXXfffcpLi5OISEhOnr0qJYtW6ZFixapVKlSadoydepUzZ8/Xz4+PqpSpYrOnTun9evXa8OGDXr33XfVp08fq/kjIiLUr18/HT16VC4uLipdurT8/f0VGhqqefPmafny5Zo2bZpVIJdSjuH+/fvr9OnTcnFxUeXKleXq6qqTJ0/qu+++019//aWffvpJBQsWzPJn819ZWW7s2LH68ccfJUn+/v6qXr26Ll26pM2bN2vz5s16/PHH9fnnn8vd3T3TzzG9Y+G/li1bpj179sjDw0OVKlVSZGSkwsLC9P3332v9+vVasmSJVVBITEzUBx98oMDAQElS0aJFVa1aNZ09e1arVq3S6tWrNXz4cL344ouZttHszJkz6tOnj8LDw2UwGFS1alXFxcVp1apVWrdunaVWtly7dk29evXSzZs3VaVKFYWHh1v214SEBL311lv666+/JEnFixdX9erVdfbsWf3555/6888/1atXLw0fPlyS9PDDD8vFxUVhYWE6c+aMypUrZ9nO2bNnLX9vduzYIZPJZHXxYtOmTUpKSlL9+vVVqlQp1a9fXxcuXNCFCxfk5+cno9Fo8weHadOm6Z9//pGPj48qVqyoiIgIhYSEaNKkSdq2bZvmzJkjV9fMB801mUx66623LB3oKlSooBIlSuj8+fP6+++/9ffff2vgwIEaMmRImmUjIiLUuXNnXbx4UeXLl9d9992nU6dOac2aNVq3bp1mzpyZ5oe9lStXasSIEYqPj5eXl5eMRqNu3bql/fv3a//+/Vq6dKlmzpyZ5u/yggULNH78eCUkJMjX11dVq1bV1atXtX37dm3fvl39+/fXW2+9leF7PXjwoPr06aNbt26pS5cuGjduXJZqBABZQbawD9mCbJEZsgXZ4k5ni+wc+9k9t5VSLqZ+8cUXiomJUfny5VWhQgWFhoZqyZIlWrdunX799Ver/dF8wVNKeRyW0WjU9evXtXPnTu3cuVN//PGH5s2bl2s3WVy/fl1du3ZVWFiYKlWqpNKlSyssLEwLFizQkSNH9Oqrr2rIkCFydXXVfffdZ9lXx48fr6tXr+rtt99Os85Lly6pW7duioiIUMWKFVWsWDEdP35cX3/9tX7//XfNmTPHUqvsfoffvHlTffv21d69eyVJZcqUUfny5RUWFqaVK1dq1apVeu+992x+/5pMJr3//vtasWKF5XvizJkz2rdvn/bt26dTp07pnXfeyVL9IiMj1b17d4WHh8vDw0Ply5eXu7u7wsPDtWTJEq1YsULTpk1Tq1at0iybne/U7By3SUlJGjZsmFasWCEpJfcajUadOXNGq1ev1l9//aVp06apdevWGb7XRYsWaezYsXJ1ddWYMWOsLi4CQFaQpexDliJLZYYsRZa6U1mqXLlyaty4sXbs2KGVK1fa7Ii2b98+hYWFydvbW48//rjl9ZxkKVvbsOd7sUCBAqpfv77Cw8N19epVFSlSRBUrVpTRaMx0W1lRtGhR1a9fXyEhIbp586ZKlSqlUqVKqWLFimrevLlKlSqlCxcuaNWqVerVq1ea5f/66y/dvHlTlStXzlIntJxkxb59++qff/5RsWLFVKlSJYWHh+uff/7RP//8o5MnT2rQoEFW8wcHB6tv376KiIiQq6urKleuLDc3Nx0/flxz5szR0qVL9fXXX1tudDLbvXu33njjDV29elVubm6qXLmy4uPjdfToUR05ckQbN27U7NmzM+yMdv36dfXu3VvHjx9XzZo1NWfOHBUuXDjT+iCHTABMJpPJdObMGZPRaDQZjUbT9u3bM5z3vffeMxmNRtP9999vunz5suX1F1980bKO77//3vL61atXTSaTybR06VKT0Wg0tWzZ0mp9q1atMhmNRlONGjVM8+bNMyUmJppMJpMpMTHRNG3aNMu00NBQk8lkMt26dcvUrl07k9FoNA0YMMB08eJFy7oiIiJM/fr1MxmNRtOjjz5qun37dpZrYG770qVLs7xMau+//77JaDSa3n77bctriYmJprZt25qMRqNp4MCBpsjISMu0+Ph404wZMyzbXb9+vWXawoULLe/h3LlzltdjY2NNo0ePNhmNRlODBg1MsbGxlmmpP8OwsLBM22YymUwfffSRyWg0mpo0aWLasmWL5fXo6GjTwIEDTUaj0dS6dWvLZzJ16lST0Wg0Pffcc1brSe+zDQ8PN1WrVs1kNBpNwcHBaWoWGBhoMhqNpg4dOqRbV7PQ0FDLfnfjxg2rac8++6zlvT/11FNW027evGm6//77TdWrVzdduXIlw/Zu377dsp6EhASraan37969e1vtd9u2bTPVqlXLZDQaTZ999lmm78XWOm0dMxcvXjQ1btzYZDQaTR9++KEpKirKMk94eLjpmWeeMRmNRtPzzz9vtV7z62PHjjXFxcVZXj99+rTp0UcfNRmNRtNHH31ktYy5Heb9xFzjxMRE09dff20yGo2matWqmXbv3m1ZJjk52VL7Hj16mE6cOGGZFh0dbRo2bJhl/4qIiLBMi4uLMz355JMmo9Fo6tKli9X+GhYWZmnj0KFDLa9n9NlkJKPlZs+ebTIajaaaNWuafvzxR1NSUpJl2u+//2564IEHLHXMqvT2LZPJZHr44Yetanz9+nXLtBUrVlimLViwwGq5L774wmQ0Gk2tWrUybdq0yfJ6YmKi6YcffjDVrFnTVK1aNVNQUFCW2picnGx67rnnLPU/ffq0Zdq+fftMLVq0sLRl6tSplmmpa9m2bVvLMXDz5k3Ld9GYMWNMRqPR9MADD5hWr15t1dYff/zRVLNmTZPRaDTNnTvXMq1bt24mo9Fo+umnn6za+fPPP1vtl0eOHLGaPnToUJPRaDTNmjXL8lp631Emk/U+PmHCBNOtW7cs9Zg+fbpl2oYNG7JUx40bN5qMRqOpadOmpmPHjlm9V/P6atasabpw4UKa9hmNRtPDDz9s2r9/v2Va6uOzW7duVtvat2+fpXYffvih1XfgkSNHLMt17tzZaj/fvXu3qXr16iaj0Wj64osvrP5mLF261DJt8+bNJpPJ9t+Rw4cPmxo1amT53khOTs5SfQDc28gWZAuyBdmCbHFvZAuTyf5jP7vntubvHqPRaOrevbvp5MmTlml79uwx1atXz2Q0Gk2ffPKJ5fWoqChT7dq1TUaj0bRq1Sqrdm/evNlUp06dNNOysn/Y+htn/ryMRqOpefPmVuf6S5YssUyrXr26aejQoZb3nZCQYNm/69SpY/W3JnV+aNSokWnr1q2WaaGhoZZavfLKK1bty853+GuvvWZpe+r3FRcXZ5oyZYplfWvWrLG5nRo1apj+97//WT7n2NhY0zvvvGM5LszfRZn5/PPPLX8bUi8THR1tGjRokMloNJrat29vtUx2v1Oze9ya/97WrVvXtGrVKktGio2NNY0aNcqSic1/p20dy4sXLzZVq1bNVL16ddOyZcuyVBsA9wayFFmKLEWWIkvdG1lq2bJlJqPRaGrRooXVezb7+OOPTUaj0fTuu+9aXstplsqN78X01mcypf/9l953lvmz+OWXX6xeNx+TkyZNsnp98uTJlvdnyyuvvGIyGo2mmTNn2pyeWk6zYp06dUzLli2zfHY3b960fF//9/vxxo0blutuzz77rNU1ucuXL1uyWIMGDaymRUZGmpo3b24yGo2mvn37Wh2/Bw4cMDVt2jRNnf5b08jISFOnTp1MRqPR1LVrV6vPGY7FUA5AFsXGxurIkSMaNWqUli9fLknq3bu3zTsKypQpo759+1r+v0iRIhmu+5tvvpEkvfzyy+rVq5elZ7HBYNCAAQPUvHlzJSUlWba7ePFihYeH6/7779fXX3+tEiVKWNZVvHhxffXVVypTpozCwsL066+/2v1ehw0bpmrVqmX6z44dOzJd17FjxxQZGSkPDw998sknKlSokGWau7u7+vXrZxkByHw3gHk5SWrVqpVKly5ted3T01MffPCBWrRooXbt2ikyMtLu92cWERGhX375RZI0ceJEPfjgg5ZpBQoU0Oeff66CBQvqwoULVney2KN8+fJq2LChJFnuFE1t2bJlkqQuXbpkuq7KlSurYsWKSkhIsKr9jRs3dODAAVWuXFlFihRRcHCwrl+/bpm+detWJSQk6IEHHsiVYUYLFy6cZr9r2rSpOnXqJCmld7q90jtmZs+ercjISLVp00Zjx461Gsa8fPnymjZtmvz8/LRr1y5t3LjRMs28/3Tt2tWqF3y5cuX0/vvv6+GHH1aZMmVstqV27dr67LPPLMMOGwwGDRo0SB07dpTJZNK3335rmXft2rXau3evAgICNGvWLFWqVMkyrUCBAho3bpzq1q2r69eva968eZZpf//9t4KDg+Xr66sZM2ZYDc9boUIFjR8/XlLK0L03btzIeiHtEBcXp++++06SNHjwYL3wwgtWIzw9/vjj+uSTTyRJCxcu1NmzZ3Nt25UrV9bEiRPl7+9vee2pp55S8+bNJVnvQ1evXrXUbtq0aWrZsqVlmsFg0EsvvaTevXvLZDJpypQpWdr+pk2btGfPHvn4+Gj69OlWo5DVrVvX8p2ckddee81yDPj6+srT01MXL17Uzz//LCnlDqbUj3w0GAx64YUXLKODffPNN5ZHBrRp00aSLHf8mW3ZskWS1KhRI0myOu6TkpIs87dt2zZL79usefPmev/99+Xj4yNJcnFxUb9+/XTfffdJyvoxbD7O6tWrZzXct8Fg0GuvvabHHntMTzzxhKKiomwu/8UXX6hOnTqW/y9XrpzlLpmDBw8qLi7OMm3q1KlKTExUixYtNHbsWKthwWvUqKFZs2bJy8tLhw8f1m+//WaZNm3aNCUnJ+vxxx/X22+/bXWXWZcuXdStWzdJSvdv5bFjx/Tyyy8rKipKL774okaPHm3XI3UAID1kC7JFdpAtsoZsQba4k9kiO8d+ds9tzdzd3fXNN99Yzt+llHNy87G/Z88ey+unTp1SXFycChUqlObxKC1atFC/fv3Uvn37LI2sYI/hw4dbnet36dLF8piVMmXKWB0Xbm5uev311yWl/H08efKkzXVOnDjRatTkypUra9q0aXJ1dVVQUJD27duX7fbu27dP69evl5Ty+aS+C9/Dw0NDhgyxjH7z38eImT3//PPq2bOn5e+up6enhg8fLhcXFyUmJurAgQNZaov5O6d9+/ZWf/MLFCigDz/8UA8++KAaNWqk2NjYNMva852a3eM2Pj5e33//vSTpvffe0xNPPGHJSJ6enho5cqTuu+8+xcTEaPXq1Tbf4/Lly/XRRx/JYDDo888/t7QPALKCLEWWyg6yVNaQpchSdzJLtW/fXn5+foqIiND27dutpiUkJOj333+XZH1c5jRLpZbd78W81LVrV7m4uOjw4cM6ceKE1bRLly5p69atMhgMevrppzNdV06z4oABA9SpUyfL/urr66uRI0dKSvn8UuezhQsXKiIiQsWKFdOMGTOsrskVK1ZMU6dOldFo1I0bNzR9+nTLtEWLFuny5csqU6aMvv76a6sRF2vXrm158tCyZcuUnJycpo03btzQyy+/rCNHjqhevXqaN2+e1ecMx6IjGmBDz54905zM161bV507d7Z0MnjmmWdsPm5MSvkBMKsXqsPDwy0/sj333HM25xk3bpz+/vtvy6PD/v77b0lShw4dbA6H6eXlpfbt20uS5Yc0e1SsWFH169fP9J/MhlOWUoZeNg/FaWuYy/j4eMuX/u3bt63aIElLlizRwoULde3aNcs0Dw8PzZ49W+PHj7c6ybbXxo0bZTKZVLp0aZuPC/Dx8dHPP/+soKAgq5Mqe5mHcg4MDLT6QxgREaFt27bJzc0tSycFkixDOZs7qEgpnVOSkpL04IMP6oEHHpDJZNLOnTst07My3LM9HnzwQZvPhjd3REn9WWVVeseMeV9/6qmnbC5XrFgxy0lx6n3dHBhGjRqlbdu2KSEhwTKtTZs2mj59ul577TWb6+zZs6fNR+6Zj88dO3bo5s2bVu1r27atpVNPai4uLpa2p27funXrLMvZ+pGkfv36+vXXX7V169YsHWfZsWvXLkVHR8vNzU0vvPCCzXk6dOigEiVKKCkpybIf5YZHHnnE5neXrX1o48aNio+PV5UqVdIdet18/Bw4cEBXr17NdPvmz61du3Y2H11Zt25d1atXL8N1/HcIbymlg1tiYqKKFy+e5sTd7MUXX5S7u7tu3LhhOU7NHdG2b9+upKQkSVJycrJ27NihggULWoayTh3G9u7dq8jISFWpUsUqIGfFo48+muY1FxcXy/DRWT2Gzd/TGzdu1IwZM3ThwgWr6V999ZU+++wzq05qZsWLF7f5+FPzvCaTyfIDVkxMjOVHnZ49e9psS7ly5Swd8tauXSsp5W+KuWapH5mQ2ptvvqk//vhDEydOTDPt+PHj6t27tyIjI/XCCy/oo48+ohMagGwhW5AtJLJFesgWZIucyutsYe+xn91z29Rq1aplM8eYL7ilvkhWtmxZubm5KSoqSh988IHlYqDZ66+/rqlTp9rMCNnl4uKS5rGRLi4ulouMzZs3T/OZmTupSbIcE6lVqFAhzaOtpJSLZ+Ybd2zVKqvMx1SdOnVs5hRJeuWVVySl/K21dSHKVvsKFy5suYAbHR2dpbaY/2bNmjVLK1eutPo8S5Qooblz52rs2LFWj0kys+c7NbvH7a5du3Tjxg25u7vbvFjv6uqq77//Xhs2bLCZwwIDAzVs2DAlJyfryy+/VMeOHdMvBoB7HlmKLCWRpdJDliJL5VReZ6nUj9w0P+4z9fYiIyNVpkwZy00iuZGlUsvu92JeKleunCX/mDtFm61YsULJyclq1aqVzbz4XznNirZeL1GihKVjY+r9x3ysderUyWZHMA8PD7300kuWeU0mk6R/j9enn37aZv5p3769li9frjVr1qT5vrh586b69Omjw4cPq27dupo9e7bN70w4jlteNwDIj4xGo9WXkYuLizw9PeXv769q1aqpbdu2qlKlSrrLZ+UL3iw8PFxSysl06h7Aqf33+ejmH7wWL16c7h/SK1euSFK6d5Jm5LXXXsvSnR/28PLyUlhYmA4dOqTTp0/rzJkzCg0NVXBwsGXUm9Qn/88884yWLFmi0NBQjR49WmPGjFGNGjXUrFkztWzZUo0aNZKbW86+wsy1t9VJwqxy5co52oYkPfbYYxo7dqwuXbqk7du3W+7oWblypZKSkvTwww/bPMm1pU2bNpo7d65VwDGPitS0aVOFh4dr3bp12r59u9q3by+TyWS5A8XeUZPSk16oNJ/g27orNzO2jplbt27p3LlzklLusPjhhx9sLmueJ/W+/u6772rAgAHav3+/evfuLR8fHzVq1EgPPvigHnroIUuAtiX1XdupmfeThIQEnTt3TtWqVbMci+vXr09zkmZm/rE5LCxMJpNJLi4uOn36tCSpevXq6bYjvZP53GKuV4UKFdI9+XJxcVHNmjV16dIlnTp1Kte2bc8+dPz4cUnSxYsX1aNHD5vLmU9KpZT3ldkdZeZ1ZlT/WrVqae/evelOt7XPmmtao0YNmyFZSnmP9913n0JCQnTq1Ck9/PDDqlatmsqWLauzZ89q//79ql+/vg4dOqTIyEi1bdtWjRs3louLi/755x8lJSXJYDBYAmd2juvcOobbtGmjxo0ba+fOnZo0aZImTZqkSpUq6cEHH1TLli3VrFkzqxHIUkt9oclWG1K348yZM5YfKWrVqpVue2rVqqVVq1ZZ9tXz589blkvvsy5atGi6+8vbb79taUNWgjMApIdsQbYwI1ukRbYgW+RUXmcLe4/97J7bppbeezb/MJ+YmGh5rWjRourbt6+mT5+u5cuXa/ny5SpevLiaNm2qFi1aqFWrVpmODmOvAgUK2NwPzHfS29pe6rvsU38GZrVr1053e+aRYMLCwrLR2hTmfTijY6VixYry8/PTzZs3derUKcuNPGaZfS7mm44y06dPH/3xxx+6fPmy3n33Xbm5ual27dp68MEH1apVK9WtWzfdThX2HA/ZPW7N+/x9991n82KQlDIyii3Xrl3T+++/b/kbTc4CkBmyFFnKjCyVFlmKLJVTeZ2lpJQOoosXL9aff/6pUaNGWc4vzSMXdu7c2XLumxtZyhZ7vxfzWteuXbVz504FBgZq6NChlvqYa5bVvxs5zYrp7T++vr6KjIy0euKN+bPI6HgyT7t27ZoiIyNVuHDhTI9RDw8P1ahRw+a0KVOmcH0nj9ERDbDhww8/tBqG317pXXi3xTzii6+vb5aXMffyDwsLy/SHNkcNF2uP/fv364svvrC6+0NKuTO0devWOnLkSJrhZP38/LRo0SLNmTNHq1atUnh4uI4cOaIjR45o9uzZKlq0qN58801179492+0y197WHRK5ydvbWx06dNDixYu1YsUKS8Ax91Y334mTFQ0aNJC/v7/CwsJ05swZlStXzjLUapMmTSzDY5tHATp06JAuX76sKlWqZHhSb4/UQyjnFlvHTOq7oLMy7G3qfb1Vq1ZasmSJZs6cqQ0bNujWrVvauHGjNm7cqPHjx6tBgwYaM2aMzR8q0huWNfV+Yt6WuY0XLlxIMxrUfyUlJenWrVvy8/O7Y/teRsxtz+xOHnP4MT9GMjfY8x2ZutapHzGTnqzcZW4eEj2j+mf2nWzrB/ec1LRNmzb64YcfFBQUpPr161t+xGjWrJmKFCkio9Go4OBgHTp0SHXr1s3RHXT21D8jbm5umj17thYsWKBff/1VISEhOnnypE6ePKkff/xRfn5+6tu3r/r375/mQok9bUj9XZBRbf9b19SPBLDnb6xZbGysHn/8cf3xxx/6448/tHr1assdWgBgD7JF7iJbkC0yQ7a48+7lbGFv/bN7bpuavY/RfOutt1SrVi39+OOP2rVrly5fvqzAwEAFBgbKzc1NHTp00MiRI3NtlAdvb+8Mp6d3005GUj8u6L/Mtc/qiGO2ZHUf9vX11c2bN7P1udjqYGdLqVKltGLFCs2YMUN//PGHLl26pL1792rv3r369ttvVaZMGQ0fPtzmRWx7vlOze9zm5DsnISFB7u7uatOmjf766y998cUXatWqVbodPgCALJW7yFJkqcyQpe68ezlLSSmj8FWqVEknT57UunXr1KFDB0VFRWnDhg1ycXGxeoR7bmSp/8rO92JeM3eqvXDhgnbs2KGmTZvq4MGDCg0NVeHChW2O1JyenGTF7FzjycrnJqV8doULF87RMRobG6vGjRvr1KlTOnv2rL744guNGjXK7vUg++iIBuQx85enPScP3t7eluck2/MHJS+cOHFCPXv2VGxsrKpUqaKuXbuqevXqqly5sqW39HPPPWfzD7mfn58GDx6swYMHKzw8XDt27NCOHTu0ceNGXb16VR999JH8/f2z/QgJ84+juXnilp7UvfpHjx6tkydP6vjx4ypcuLAeeuihLK/HYDCodevWWrFihbZs2aIWLVooPDxcderUUYECBVSjRg0VLlxYJ0+eVEREhOUum9wa7vlOSv3jdWBgYJq7jTNTo0YNTZo0SQkJCdq/f7927NihrVu3as+ePdq9e7d69+6tP//8M80JzO3bt20Ow5s6QJnvAjC38aOPPrI8PtGe93Yn9r30mH9UyexHEHNgyE5HntxgrlX79u01derUXF2nrUe+mGXns8lJTc0d0bZs2aLBgwdr69atklI6okkpQ60HBwdrx44dKlasmI4fP64SJUpkODLAneDh4aGXX35ZL7/8si5evKjt27drx44d2rRpk65cuaIpU6bIy8tLL7/8cra3kbpON27cSPdOqqioKKv5Ux/bN2/etHukh4EDB2rIkCEaM2aMFixYoDFjxqhJkya5PmIEAOQmsgXZgmxhG9nCscgWWa9/ds9tc6pdu3Zq166dbt68qZ07d2rnzp3auHGjTp48aXn84/Tp09MsZ6vzVExMTK60yR4ZbdOc67Iy4kJ6sroPm6c7eh8uWrSohg8fruHDhys4OFg7d+7U9u3bFRQUpHPnzmnw4MH6+eef0x0pJCuye9zm5DvH3d1d3377rZo3b65u3brp6NGjGjFihP73v/9l+dF5AOAoZCmyFFnKNrKUY93LWcqsc+fO+vLLLxUYGKgOHTpo9erVio+PV+PGja1uWMjtLJWT78W85OXlpSeeeEKLFi1SYGCgmjZtahkN7cknn7T7xqXsZkV7+Pr6KioqKsP93Py5meeX/v07m51jtHnz5po2bZo2bNigIUOG6KefflL79u3VtGlT+98AssX+W9AA5Crz3Q8xMTHp/jFbu3atXnrpJX322WeSUoa/l/4dCtWWsLAwHTx4MFvPgc9N//vf/xQbG6tKlSppyZIleuWVV/Tggw9aDdl56dKlNMtdvXpVu3btsrS/QoUK6t69u7788ktt3LjRMuyq+Y9rdphrn1Edp06dqj59+mjZsmXZ3o6U0qu/cuXKiomJ0ZYtW/TXX39Jkp566im7TwratGkjKWWoZ/Pz0M1377i4uFj+iO7YscPy/OzcGu75TipYsKBlKOzQ0NB05wsODtbRo0ctJylJSUkKDw/XP//8Iynlh86GDRvq9ddf14IFC7RgwQK5uLjo8uXLls4+qaW3P5iHdPbx8VGZMmUkZe1YvHDhgvbt26eIiAjLa1nZ9/r376+BAwfqwIED6c6TE5UqVZKUMvR5eh2ykpOTdeTIEUkpx2BeyEqNb9++rZ07d+rMmTNZetyJOSwHBwenO09G09JjrunRo0fTHar55s2bljskU9e0UaNGKliwoA4ePKiIiAjt27dPAQEBlmHnzR3Stm/fbhkNrU2bNnn6Y31UVJT27dtnucusZMmS6tSpk8aPH68NGzZYfoDLyfe0lPJYF/P35KFDh9KdzzzNXNdy5crJYDBISn//OXjwoHr06KFhw4aluchmvttq6NChKlWqlK5du6bRo0fn6L0AgKORLcgWZAvbyBZkC8kx2cLeYz+757bZFRsbq2PHjln2OT8/P7Vp00YffPCBVq9erbfffltSymOMzBcFzOfQkhQfH59mnan3vzslo/oePXpUkjJ8NFtmzPvw4cOH053nxIkTlg5xjtyHzY8LMz9Gplq1anrppZf07bffau3atSpTpoySkpK0atWqHG0nu8et+TgKDw+3etxOaj/99JN69+6t2bNnW71epEgRtW7dWm5ubvrkk09kMBi0Y8cOLVy4MEfvBQByA1mKLEWWso0sRZaSHJOlzDp16iSDwaCgoCDdvHlTgYGBktKOUpjbWSq734v5gbk2a9euVVJSkuXx0PaM7JidrJhdWclb5s+tUKFClk6omR2jCQkJ6tGjhwYPHqwzZ85YTXv88cfl5eWlxx57TG3btpXJZNKIESPytOPpvYaOaEAeq1y5suVkaenSpTbnWbZsmXbu3Gk52Tdf3F+yZInN57wnJiZq4MCB6tatmyZOnOiglmeN+bnwlStXtvl4hi1btuj8+fOSZHVi0qdPH73wwgs2g4Wvr68eeOCBNMukfrxDVh550KpVK7m6uurcuXPatm1bmumxsbFasmSJgoKCMl2fedsZzWc+Afj7778tJwVZfVZ3ai1btpSHh4e2b99uOUFP3YPbHHZWrVqlw4cPKyAgIMujJtlbQ0cz34X0448/2uzYc+PGDfXq1UudOnXS//73P0kpJySPPvqoevXqpcuXL6dZpl69epbe9LbWuWTJEptt+emnnySlHH/mIWfNx+Lvv/+e7jPGhw8frmeffVZDhw61vNa6dWtJKSeJ5sdEpnbs2DGtX79e69ats5xwZfezSW+5Bg0aqFChQkpMTNSCBQtsLvvbb7/p8uXLcnFxUcuWLe3aXm7tP61bt5bBYNDJkyctj6v8r3nz5umll17S008/rdu3b2e6TvPdeevWrbP5I9CJEye0a9cuu9vaqlUrubm56fLly/r9999tzvPjjz8qMTFR3t7eaty4seV1Nzc3tWrVSklJSfruu+8UHx9v6XwmpXRUc3d31549e7RmzRpJtn+4MHdMuxPHr3nfnjlzZppp7u7ulvdnT+i0xcfHx/IYhh9++MHmPGfOnNG6deskpXwOUkpwatCggaT0/76uWrVKe/bs0dmzZ9Pt1Ofn52fpgGZ+RCcA5FdkC7KFvcgW/yJbZI5skZa9x352z22za9GiRXr66af17rvv2qyj+RiXUr7vJVmNPHHy5Mk0y5gv2N5Jhw4dslwgSe3YsWOWRwO1b9/e8rq9+7j5+Dtw4EC6jxqaN2+epJQbcKpVq5blttsjMTFRnTp1Uq9evSw3IKVWrFgxy41V6d38lFXZPW4bNGggHx8fxcfHWy4SppacnKwlS5Zo27ZtGY5kV6tWLfXu3VuS9MUXX6S5cAQAdxpZiixlL7LUv8hSmSNLpS8gIEAtW7ZUfHy8li5dqt27d8vX1zfNKIu5naWy+70oOf46TGYDENStW1dVq1bV9evXNX/+fJ0/f141a9ZU9erVs7yN7GTF7DIfo8uXL7ca+cwsPj7ecmyn3sfNx2hgYKDNm6Q2bdqkPXv2aPPmzRmOkD1q1CgVKFDA8ohO3Bl0RAPymIuLiwYOHChJmjlzphYvXmz5wk9KStL333+vv/76S25ubpYfaF544QUVL15c4eHhGjBggOUPoSRdu3ZNb775pk6cOCF3d3e98sord/w9pWbuJb9lyxarjh2JiYlatWqV3nrrLctrqcPa008/LUn65ptvtGnTJqt17tq1y3KHjfmPkGT9GLbUNUlPuXLl9OSTT0qS3nvvPe3du9cyLSoqSu+9954uXbqkMmXKqEOHDhmuy7zt6OjodO9a6NSpk9zc3PTHH38oODhY999/v10nBWa+vr5q3Lixbty4oTVr1sjT01P169e3TDd3XtmwYYNMJpNdoybZW0NH69evn3x8fLR79269++67Vp2Gzp07p379+un69esqUKCAXnjhBUlS9erVZTQalZSUpKFDh+rixYuWZeLj4zV58mTdvHlTPj4+atiwYZpt/vnnn/rqq68sJ1YJCQmaPHmy1qxZIw8PDw0aNMgyb4cOHWQ0GhUdHa0+ffpY9cq/efOmPv74Y23dulUuLi7q16+fZVrHjh1VsWJFRUdHa9CgQVZtPHnypN555x1JKT+km4cezu5nk95y3t7eljZNnTpVCxYssAp8a9as0ciRIyVJ3bt3txzLWd1eRseCPcqUKaNnnnlGUsrIVOYQIaUE1MWLF+ubb76RlPLdmPo58ul5+OGHdf/99+vmzZt6/fXXLSN6SVJISIgGDhyYrR/1S5Uqpe7du0tKGQb8jz/+sGrrwoUL9fXXX0tKeexjgQIFrJY330W3ePFiSbLqiObj46O6devq9u3b2rFjh/z8/Kw6spmZw3tERESOw0FmzN/TixYt0vLly63CyvHjxzV//nxJ1t/T2TVo0CC5ubkpKChIH330kdW+dezYMb366quKi4tT9erVLSOZSSl1dnFx0YoVKzR9+nSrmixfvtzSxr59+2a4/datW6tjx46SpDFjxuT5XawAkB6yBdnCXmSLFGSLrCFbpJWdYz+757bZ8fjjj8vd3V0hISH69NNPrToGXbt2TZMnT5aUciHDfHHtvvvus/yI/9lnn1keA2QymRQYGKhvv/02R23KDpPJpMGDB1sdF8eOHdOgQYNkMpnUuXNny2jSkv37eL169Sx/AwYPHmwZ1URKOdanTp2qX375RVLK5+yokand3Nz0xBNPSJLGjRuXZuSNP//8U0FBQZJy3kkxu8etn5+f5Rxi/PjxVsdRbGysxo0bp0OHDsnPz0/PPvtshm0YPHiwKlSooJiYGA0fPjxfXGgHcO8iS5Gl7EWWSkGWyhqyVMbMnUG/+uormUwmPfbYY2ke1yrlbpbK7vei9O91GEcdn+b3bu4sZ4u5U+1XX30lyf4OtdnJitnVo0cPlShRQleuXNFrr71mdRPK1atXNWTIEIWEhMjX11dvvPGGZdrzzz8vf39/hYeH65133lFkZKRl2oEDBzRq1CjL+m3tL2YBAQF67733JKV0Zt2+fXuO3g+yxi2vGwBA6tatm0JDQzV37lx9+OGHmjJlikqWLKmzZ88qMjJSBoNBH3/8seVkuFChQvruu+80YMAAbd26VY888oiqVKkiFxcXnTp1SvHx8XJzc9OkSZOydZfmjBkzLB0hMtO/f/8MOxm88sorWrVqla5fv64XXnhBFStWlK+vr86ePauoqCj5+PioXr162rt3r9VJXs+ePbV161Zt2rRJr776qgICAhQQEKDr169b/vC2adPGcuIjSf7+/ipTpozOnTun119/XZUqVdKQIUMy/HFu5MiRunDhgnbu3KnnnntOFSpUkI+Pj06dOqXY2Fj5+/tr6tSp8vLyyrAO1apVk6urq2JjY/XYY48pICBAs2fPtvrjXLRoUbVu3TpHd9mYtWnTRkFBQUpISFCzZs0sd35IKcGtbNmyliHE7RnuuWLFivLx8VFMTIy6d++usmXLaty4cdkKYrmhQoUKmjJlit566y2tWrVKa9asUZUqVZSQkKCwsDAlJibKx8dH33//vVVv98mTJ+u5557Tzp071bZtW5UtW1be3t46e/asoqOjZTAYNGbMGBUpUiTNNo1Go6ZNm6aFCxeqXLlyOnPmjCIjI+Xp6akJEyZYhpCVUkZ9mjZtmvr27aujR4+qY8eOuu++++Tt7a2wsDDLiduwYcOs9kMPDw99++236tu3r3bt2qU2bdqoatWqiouL0+nTp5WUlKSaNWtaPQYwu59NRsv16dNHZ8+e1U8//aQxY8bo66+/Vrly5XTx4kXLENXt27fXiBEjsvyZZeVYsNfw4cN16dIlrV+/XgMGDFBAQIBKlCihc+fOWUJv+/bt9eabb2ZpfQaDQZMnT9aLL76oPXv2qG3btqpataoSExMVGhqqggULqmLFigoLC7N6LE1WDBs2TJcuXdLatWs1ZMgQBQQEqGTJkjpz5ozlrqoXX3xRr776applW7VqJXd3dyUkJEiy7ohm/n9zIGrdurU8PDzSrKNGjRqSUgLKo48+qoCAAP30008OuVDy6KOPqnv37vrll1/0/vvva+LEiSpVqpRu3ryp06dPy2QyqU6dOurfv3+Ot1WvXj2NGzdOH374oX755RetXLnSMoz+qVOnJKUcu998841VXZo1a6Zhw4ZpwoQJmjx5subMmWPZx69cuSJJev3117PUWW7EiBHasmWLrl27po8//lhTp07N8fsCAEcgW5At7EW2IFuQLbKfLST7j/3snttmR0BAgD799FO9++67+uGHH7RkyRKVL19eSUlJOn36tOLi4lS4cGGNGzfOsoyrq6vefPNNffTRR9q5c6dat26t++67TxEREbp8+bIefvhhXbt2Tfv3789R2+xRsmRJXb9+XU8++aSqVq0qk8mk0NBQmUwmNW3aNM1+lZ3v8M8++0z9+/fX3r171bNnT5UpU0ZFihTRqVOndPPmTRkMBr355puWjmKO8tZbb2n37t06cuSInnnmGZUpU0aFCxdWRESE5Vjq0aNHjjuiScr2cfv666/r1KlTWr16tQYMGKBSpUqpSJEiCgsL061bt+Tl5aVJkyYpICAgw+17eXlpzJgx6t27t3bu3KmFCxdaLl4DQF4gS5Gl7EWWIkuRpXKWpcwefvhh+fv7WzoapXdc5maWyu73ovTvdZg9e/boscceU5UqVSwd8XJDzZo1tX79egUGBio4OFgNGza0dLoye/rpp/Xll18qJiZG7u7ulhvpsyo7WTG7ChYsqOnTp6tfv37au3evHn30UVWpUkVubm46fvy4EhIS5O/vry+++MLyOE4p5fv6m2++0cCBA7VmzRqtX79eVatWVXR0tM6ePSuTyaQWLVpoyJAhmbbhmWeeUWBgoHbu3Knhw4crMDDQ0qEQjsGIaEA+8cEHH2ju3Ll65JFHZDKZdOzYMRkMBj322GNatGiR1Ym8JNWuXVuBgYF6/fXXVa1aNZ09e1YnT55UsWLF1KlTJy1dujTNsKVZFRYWpj179mTpn/SGuTUrXbq0Vq5cqR49eqhixYq6cOGCTp06pWLFiumll17SypUrLSclO3bssJwQGgwGffvttxo+fLjq1atneVb17du31aJFC3322WeaNm2a3Nys+9N+9dVXqlevnpKTkxUWFqbTp09n2D4/Pz/NnTtXY8eOVYMGDXTt2jWFhoZata9WrVqZ1qxChQoaP368KlasqMjISF24cMFmT3XzyZOHh4fdJwWpPfLII5b//m9nFenfIVP9/PwsQ9Vmha+vr7766itVr15dMTExOnv2rCUo5ZXWrVvrt99+U+/evVW+fHmdOnVK4eHhKlOmjJ5//nmtXLnS6k4jSapSpYqWLVumHj16qEyZMjp//rylg1HXrl21YsUKy11W//X+++9r5MiRKl68uIKDg+Xt7a0uXbpo2bJlNu+4KleunJYtW6b33ntPdevW1eXLly0999u3b68ff/xRvXr1SrNclSpVtGLFCg0cOFCVK1dWWFiYzp8/r6pVq+qdd97RokWL5O/vb5k/u59NRsu5uLjo448/1uzZs9W2bVsZDAYdPXpUUsqJ/7fffqupU6daBejMZPVYsIenp6e+++47TZ48WS1btlRCQoKOHj2qpKQkNWnSRBMnTtSUKVPs6jRWoUIFrVixQr169VKpUqV04sQJXb16VU8++aSWLl2qsmXLSpLNIZkzYg6vkydPVosWLRQfH6+jR4/K29tbTzzxhH744Qd99NFHNjuGFShQQI0aNZKUEkxLlixpNT31UMjp/XDRtGlTvffeeypTpowiIiJ09uxZS4crRxg9erTGjx+vJk2aKDk5WcHBwYqMjFSDBg00cuRILVy40O67n9LTqVMnrVixQt27d1exYsV0/PhxXb9+XfXr19fIkSO1ZMkSy51pqfXq1UuLFi1Sx44d5enpqeDgYCUmJqp169aaO3euBg8enKXtFylSRMOHD5eUcidaeo9fBYD8gGxBtrAH2YJsQbbIWbbIzrGf3XPb7Hjqqac0f/58tW/fXgULFtSJEyd07tw5VahQQa+99pp+//13Va1a1WqZ7t27a+bMmWrevLkMBoNOnDihYsWK6cMPP9S0adPsvmEnp0qXLq3Fixfrscce06VLl3T+/HnVrl1bo0eP1uzZs9OMNi3Z/x3u7++v+fPna+zYsWrUqJFu3Lih4OBgFS5cWN26ddOSJUusRtBwFF9fX82fP1+DBw/W/fffr8jISB07dkwmk0mPPPKIZsyYoY8//jhXtpXd49bNzU2TJ0/W5MmT1bx5c92+fVvBwcHy8/NTly5dtHz58iyPjN20aVN169ZNEo/oBJA/kKXIUvYgS5GlyFI5y1JmHh4eln2hQoUKNkfJM8utLJXd70VzG/r27avixYvr7NmzOnr0aLaespOeV199Vc8884z8/f0VFham4ODgNPMUKVLE0rGyTZs22epkmJ2smF01a9bUqlWrNHDgQFWtWlVnzpxRWFiY7rvvPvXv318rV660+ejZRo0aadWqVerVq5dKly6t0NBQXb16VXXq1NGYMWM0c+bMLB0bLi4u+uSTT+Tl5aVz587p888/z5X3hfS5mJx0zOtTp07p22+/1e7du3X16lWVLFlSjz/+uF577bUMh+bLiokTJ2rOnDkaNGiQ1fCAZkFBQerTp0+6y/v4+FgNLQvg3jF//nx98skneuyxxyzDpSJ/MN+VNnfuXKsOP7g3de3aVYcOHdIXX3yRbhgGgDuNjAMgNbJF/kW2AHLH119/rW+++Ub169fXTz/9lNfNAZDLyDcA8gpZKv8iSwFZ16VLFx0+fFjff/99lm8EAe4Up3w054EDB9SrVy/FxMSoTp06ql27tvbs2aPp06dr/fr1ORqdY8uWLZo7d26G8xw5ckRSyp0QqYcPNLOnxzIA52IeSrt79+553BLg3rVkyRLNnDlTLVu21Icffphm+qVLlyx3mNSsWfNONw8AbCLjAPgvsgUAALhbkW8A5CWyFIC73eHDh3X48GGVKlVKLVq0yOvmAGk4XUe0xMREDR06VDExMRo3bpxlaO/Y2Fi99dZbWrdunSZNmqSRI0fave5r167p/fffV2aDyB06dEiS9Oabb3LgA/e4mJgYnTlzRl5eXpo9e7aCg4NlNBq5kwPIQ7Vq1VJYWJjOnDmjunXrqmPHjpZHZZ49e1bvvPOOEhIS1LRpU1WuXDmPWwsAZBwAKcgWAADAGZBvANxpZCkAzsD8GPsrV67ogw8+kCT17NkzW49DBRzNNa8bkNt+++03nTlzRs2aNbMEGEny8vLSp59+Kh8fH/3yyy+Kioqye93Dhw+3PGM4I+a7abLyvHIAzu3mzZt66qmn9Oijj2rRokVydXXVqFGjLJ1eANx51atXV8+ePZWUlKR33nlHzZs3V9euXdW+fXu1a9dOe/fuVZUqVTRx4sS8bioASCLjAEhBtgAAAM6AfAPgTiNLAXAGv//+u9q2bavnnntOYWFhMhqNevHFF/O6WYBNTtcRbd26dZKkdu3apZlWuHBhNWnSRAkJCdq8ebNd612wYIHWr1+v119/PcNwEh0drTNnzqh8+fLy9/e3axsAnE/x4sVlNBrl5eUlo9GoadOmqWHDhnndLOCeN2LECM2bN09t27aVj4+Pjh8/rqioKNWuXVsffPCBlixZopIlS+Z1MwFAEhkHQAqyBQAAcAbkGwB3GlkKgDOoXr26AgIC5OPjo7Zt22rOnDny8PDI62YBNjndozlDQkIkSdWqVbM5vUqVKlq/fr2OHTumjh07Zmmdx48f18SJE1W/fn299tprmjBhQrrzHj58WJJUvnx5ffvtt/rjjz90+vRp+fn5qVmzZho4cKAqVapk57sCcLdycXFRYGBgXjcDWRAcHJzXTcAd1qxZMzVr1iyvmwEAmSLjAJDIFncTsgWQO9544w298cYbed0MALmMfAPgTiNL3T3IUkD6WrdubXdHfSCvON2IaBEREZKkEiVK2JweEBBgNV9m4uLiNHToULm7u+vzzz/P9Bm75hATFBSkGTNmKCAgQE2aNJEkBQYGqmvXrtq2bVuWtg0AAAAAZBwAAAAAzoJ8AwAAADg3pxsRLSYmRpLk5eVlc7r5dfN8mfnss88UEhKiiRMnqmzZspnOf+TIEUlSo0aNNGXKFBUrVkySFB8frwkTJmjBggV688039ddff6lgwYJZagMAAACAexcZBwAAAICzIN8AAAAAzs3pRkQz3+3i4uKS4XwmkynTdW3YsEE//vijOnTooE6dOmVp+xMmTNDq1as1ffp0S4CRJA8PD40YMUI1atRQZGSkVqxYkaX12ZKVtgMAAABwDs6eccg3AAAAwL3D2fONRMYBAADAvc3pRkTz9fVVZGSkbt++bXN6bGysJMnb2zvD9Vy5ckXDhg1TqVKlNHr06Cxv38PDQ5UqVbI5zWAw6KGHHtLRo0d18ODBLK/zv5KTTYqOztrdQLnNYHBVwYLeio6+raSk5DxpgzOjvo5HjR2L+joW9XU8auxY1Nfx7uYaFyzoLYPB6e6TyRXOnnHIN86NGjsW9XUs6ut41NixqK/jUWPHutvrS8axzdnzjUTGcWbU17Gor+NRY8eivo5HjR2L+jrW3V5fe/KN03VECwgIUGRkpC5fvqxy5cqlmR4REWGZLyPTpk3TtWvXVKNGDY0ZM8Zq2uHDhyVJf/75p8LDw1W5cmUNGDAgS+0rVaqUJKUbsrIqMTFvd8ykpOQ8b4Mzo76OR40di/o6FvV1PGrsWNTX8aixc7kXMk5e768cM45HjR2L+joW9XU8auxY1NfxqLFjUV/nci/kG4mM4+yor2NRX8ejxo5FfR2PGjsW9XWse6G+TtcRrVq1agoJCVFoaKjq16+fZnpoaKhlvozExKTcrXL06FEdPXrU5jwhISEKCQlR48aNNWDAAMXFxWnMmDG6du2aPv30UxUuXDjNMhcuXJAklSxZ0q73BQAAAODeRMYBAAAA4CzINwAAAIBzc7pxoVu3bi1JWrNmTZpp169f144dO+Tu7q7mzZtnuJ4JEyYoODjY5j89e/aUJA0aNEjBwcGaP3++JMnT01NbtmzRunXrtHbt2jTrjI+P12+//SZJeuihh3LyNgEAAADcI8g4AAAAAJwF+QYAAABwbk7XEa1du3YqXbq0goKCtGDBAsvrsbGxGjFihGJiYtStWzcVK1bMMi0hIUEnTpzQiRMnlJCQkKPtP//885Kkzz//XMeOHbPa/rBhw3T69Gk1atQo0xAFAAAAABIZBwAAAIDzIN8AAAAAzs3pHs3p5eWlCRMmqF+/fhozZoyWLl2qsmXLau/evYqIiFDNmjX1zjvvWC1z6dIldejQQZK0du1alS1bNtvbf/nll7Vnzx6tX79eXbt2Vf369eXv76/du3fr6tWrqlSpkiZPnpyj9wgAAADg3kHGAQAAAOAsyDcAAACAc3O6jmiS1KRJEy1evFjffPONdu7cqdDQUJUtW1bdunVTnz595Ofn57Btu7u7a9q0aVq8eLGWLl2qQ4cOKTk5WeXKldPzzz+vV155RT4+Pg7bPgAAAADnQ8YBAAAA4CzINwAAAIDzcjGZTKa8bgTsk5SUrGvXbuXJtt3cXFW4sK+uX7+lxMTkPGmDM6O+jkeNHYv6Ohb1dTxq7FjU1/Hu5hoXKeIrg8E1r5uBPEC+cW7U2LGor2NRX8ejxo5FfR2PGjvW3V5fMs69i4zjvKivY1Ffx6PGjkV9HY8aOxb1day7vb725BtSEAAAAAAAAAAAAAAAAAAgR+iIBgAAAAAAAAAAAAAAAADIEbe8bgAAAEB+lJSUqOTku29o3PwsOdlFsbEGxcfHKSmJp8M7Qn6osYuLiwwGN7m4uOTJ9gEAAJCWyWRSUlKiTCbOw3NTfjj/dmb5pb6urq4yGLiUAgAAkJ9wDSf35Zfzb2eVX+p7J67hkJ4AAABSiY6O1qVLF5WQEJ/XTXFKV664Eg4dLD/U2MXFVR4eXipQwF9ubu552hYAAIB7WXx8vK5ejVBs7G2ZTJyHO0J+OP92Zvmlvm5uHvL1LShvb9+8bgoAAMA9jWs4jpVfzr+dVX6pr6Ov4dARDQAA4P/FxNxSVNQVGQye8vcvLoPBIIlRnXKTweDCnTQOlrc1Nik5OVkJCXG6ffuWrl69qMKFA+Th4ZlH7QEAALh3xcXF6tKls0pONsnXt4Dc3T3l6uoqMk7uIuM4Vt7X16SkpCTFxNxUVNQVSaIzGgAAQB7hGo7j5f35t3PL+/remWs4dEQDAAD4fzduRMrd3UuFChXjsYIO4ubmqsTEvL/bw5nlhxp7enrLx6egrl27pJs3I1WkSIk8bQ8AAMC9KDo6Ui4uripaNOD/O6DBEfLD+bczyw/1dXdPyTjXr1/WrVvRdEQDAADII1zDcbz8cP7tzPJLfR19DYdfIAAAACQlJSUqISFevr5+BBggF7i6usrXt4Di42OVlJSU180BAAC4pyQlJSku7rZ8fQvSCQ3IBS4uLvLx8VViYrySkhLzujkAAAD3HK7hALnLkddw+BUCAABAsjyT3WBgwFggtxgM7pKk5GQ6ogEAANxJ5vMvd3f3PG4J4DzMvxeYfz8AAADAncM1HCD3OeoaDh3RAAAAADgEd6YBAAAAcB7kGwAAAADOw1HXcOiIBgAAAAAAAAAAAAAAAADIETqiAQAAAAAAAAAAAAAAAAByhI5oAAAAgBMxmUx53QQAAAAAyDVkHAAAAADO4l7IN2553QAAAADcnU6fDte8ebO0f/9eXb9+XQEBAXr44bbq2fMVeXt727WurVuDtHTpLwoOPqIbN26oUCF/1a5dR88/30v3318rzfw7d27X0KGD0l2ft7e3/vprs93v6W534MA+TZ36pWbNmm/1eosWDSVJq1b9LX9/f0lSt25P6uLFC5o16wdVr17zTjcVAAAAyHfIOPnP/v37NGnS55o9m4wDAAAA2IN8k//cK/mGjmgAAACw29GjhzV48ADdvh2jGjXuV40aNXXw4AHNnz9XW7cGadq0mfL19cvSumbM+Fbz58+Vi4uLqlWrodq1A3T6dJg2blyvoKBN+uCDj/T44x2tlgkOPiZJqlGjpsqVq5BmnR4eHjl/k3eZW7duauDAvnndDAAAAOCuRMbJf27duqnXXnslr5sBAAAA3HXIN/nPvZRv6IgGAAAAuyQmJmrUqOG6fTtGH3zwkTp2fFqSFBcXq1GjhisoaJNmzPhWQ4e+n+m69u/fp/nz58rLy0uffTZF9es3tExbvnypvvhivD7//FPVq9dQJUuWtEwLDj4qSXr11YFq3LhpLr/Du1NycvrDOS9YsESSVKBAgTvVHAAAAOCuQcbJn8g4AAAAgP3IN/nTvZRvXPO6AQAAALi7rF37p86fP6cGDRpbAowkeXp6adiwkfL29lZg4HJFR0dnuq5Vq5ZLkp5/vqdVgJGkTp26qlmz5oqPj9eGDX9bTQsJSbmbpnr1Gjl8N/eGChUqqkKFijIYDHndFAAAACDfIePcfcg4AAAAgG3km7uPs+UbOqIBAADALkFBmyRJrVs/nGZaoUL+qlevoRISErRjx9ZM1+Xp6aXKlauoQYNGNqeXL19RknT58mXLazdu3ND58+dUpkxZFSxYKBvvwFpSUpKWLl2kl19+Xm3bttDTT7fXF1+MV3R0tPr0eUktWjRUZGSkZf5Bg/qpRYuGWr/+7zTrunDhvFq0aKh27VqmmXbx4gV99dWXeuml7mrXrpUeeqipnn76MX344Xs6dOiAzfUMGTJQ0dFRmjLlc3Xt2lEPP9xMXbo8oSlTPrdq0+zZM/T44/9+Hi1aNFSLFg3T/H/qZdJjMpm0evUqvf76q2rfvrXatGmul17qrnnzZun27duZLg8AAADcbcg4ZBwAAADAWZBvyDd5jUdzAgAAwC4nT4ZKkipXrmJz+n33VdLWrZsVGnpc7do9luG63nnngwynHzlySJJUokQJy2vmO2nKlCmnefNmad26v3Tu3Fn5+vqpYcPG6t27jyX8ZCYpKUkffDBU27Ztkbe3t+rXb6SEhHitWrVCBw/uV2JiYpbWk5nDhw9p6NDXdevWLVWseJ8aNWqiuLg4BQcf1YYN67R580ZNmTJN9eo1sFouOjpS/fr11uXLEapVq44qV66qPXv+0ZIli7R79z+aM2eB3N3dVaVKVbVp007r1v0lSXr00cez1c6kpCR99NEH2rRpvTw9PVWjxv0qWLCQDh7cr1mzpmvDhnX66qtpKlTIP6clAQAAAPINMo79yDgAAABA/kS+sR/5JnfREQ0AACCLTCaTYhJj8roZ2eLj5iMXF5dcWdeVKyl3thQrVtzmdPPr5vmyKyhokw4e3C83Nze1avXvnSLBwUclSTt3btO+fXv0wAP1VKJESQUHH9Off67W5s0bNGHCpHTv0Elt8eKftG3bFpUtW05fffWdSpQoKUk6fjxEQ4cO0vXr13L0Hsy+/HKCbt26pf79B+nFF3tbXo+NjdWoUcO0ZctmLV36S5oQc/x4iKpUMWrBgiUqWbKUJOncubPq27enTp06qU2b1uuRRx5V69ZtVL9+I0uIGTlybLba+cMPc7Rp03oZjdU0btznKlWqtCQpLi5WEyeO059/rtZnn43TuHGfZ2v9AAAAyD/IN/8i49iPjAMAAID8hoyTgnxjP/JN7qIjGgAAQBaYTCZ1XPao/rm4I6+bki2NSzZVYOc1uRJkzEP7enl52Zzu6elpNV92nDgRqk8/HS1JeuGFXpYTeEkKDk65m+aBB+przJjxKlKkqCQpPj5e33wzWb/+ulgjR36gn39ergIFCmS4nWXLlkiShg593xJgJKlqVaOGDHlbH388ItvvwSw2NlZVqxpVokQJPffci1bTvLy81LHj09qyZbMuXDhvc/nBg4davf8yZcqqZcvW+v33QIWGHtcjjzya4zZKUkJCghYtWihJ+vjjcZYAI6UMv/3uu8O1c+d2bdq0QWfOnFa5cuVzZbsAAAC488g31sg49iHjAAAAIL8h4/yLfGMf8k3uc82zLQMAANxlXJR7d9zfzVxdU04hMwtEJpMpW+s/cuSQhgzpr+joKD34YEv16fOa1fQPPxythQuX6LPPJlsCjCR5eHhoyJB3VLWqUVFRUVqz5rcMt3Pp0kWdO3dWXl5eatiwcZrpDz30iDw8PLP1HlLz8vLSsGEjNX78l3Jz+/c+kOjoKO3du1s7dmyTJCUkxNtcvlatOmleM9+xlJOg+F/Bwcd08+YNBQSUsDkstre3t+rVayCTyaQ9e3bl2nYBAACQN8g3/yLj2IeMAwAAgPyIjJOCfGMf8k3uY0Q0AACALHBxcVFg5zUM6yzJx8dX0dFRio2NtTk9Li5OUspJr73Wr/9b48Z9rNjYWLVs+ZBGj/7UEprM3N3dbZ5kS5LBYNCDD7bU8eMhOnr0SIbbunr1iiSpWLGANNuQJDc3N5UuXVphYafsfh+2HDt2RCtXLlNISLDOnTurGzeiJf0bBm2FPi8vL3l4eKR53WAwSJKSk5NypW1SSqiTpIiIS2rRomGW5gUAAMDdiXzzn/WRcbKFjAMAAID8goyTal3km2wh3+QeOqIBAABkkYuLi3zdffO6GXmuePHiio6O0tWrV1S6dJk0069cuSxJKlasmF3rnTdvlmbPniGTyaTOnZ/Rm2++YzlZt0dAQAlJUmxszu80MRjsO11OTk62+fo330zRzz//KEmqUKGiGjduqooV71O1ajWUnJykDz542+ZyuXlxLTMmU0rbixYtqgYN0t5dlFqFChXvQIsAAADgSOSbf5Fx0kfGAQAAwN2CjJOCfJM+8s2dQUc0AAAA2KVy5ao6cSJUp06dVO3addNMP3XqpGW+rEhOTtb48WO0evUqGQwGDRr0lp555jmb88bFxWny5M8UGXldw4aNVKFC/mnmiYi4JEkKCCiZ4XZLlixlmT8xMdFqyGWzK1ci0rxmvvPGVmCJjo5K89r+/fv0888/ys/PTxMnTlbduvWspm/cuC7Ddt4pRYumhM4CBQpq5MixedwaAAAA4M4h45BxAAAAAGdBviHf5LW049cBAAAAGWjatLkkacOGtCffUVGR2rt3l9zd3dWoUZMsrW/ixE+0evUqeXt7a8KEL9MNMJLk6empnTu3KyhokzZv3phmekJCgv7+e40k6cEHm2e43SJFiqpy5SqKj4/Ttm1BaaYfPLhfUVFpQ4l5uOpr167ZWOaAjdf2SZIaNmySJsBI0vbtWyXZHtbZHjm986ZGjfvl5eWlM2dO6/Tp8DTTTSaThgwZqP79X9G+fXtytC0AAAAgPyHjkHHIOAAAAHAW5BvyTV7nGzqiAQAAwC6tWz+kEiVKaufObfr118WW1+PiYjVhwljdvn1bTzzxtIoUKWqZlpiYqPDwMIWFnVJiYqLl9dWrV+m331bKYDBowoRJatasRabb79z5GUnSd99NVWjocavtf/rpaJ07d1YPPFBfjRo1zXRdL77YW5I0efLnOn06zPL6lSuXNXHiOJvLVKlilCQFBi5TTMwty+vBwcf0ww9z0szv7+8vSTp06ICuX/83+CQnJ2vJkp+1atWK/29/fKbtzYiHh4flv6Ojo+1e3svLS507P6OkpCSNHv2hzp8/Z5mWlJSk6dO/0e7dOxUeHqZq1WrkqK0AAABAfkLGIeOQcQAAAOAsyDfkm7zONzyaEwAAAHbx9PTSiBEf6913h2jSpIlatWqFSpcuo0OHDujKlcsyGqtpwIBBVstcvhyhF17oJklavHilSpUqraSkJM2c+Z2klDtbfvttpX77baXNbTZq1ESPP95RktSjx4s6dGi/tmzZrD59XlTt2nVVqFAhHTiwX9evX1OFChU1evSnWXov7do9psOHD2nJkp/Vq1cP1avXUB4e7tq9e5cKFSpkc5mnn+6iZcuW6OTJE3r22c6qXbuuoqIidfDgfjVr1lyHDx9UXFycZf42bdpp3rzZunjxgnr06KI6derJ1dVFwcHHdPlyhO67r5LCwk4pMvK6kpKSZDAYstT2//Lw8FBAQAlFRFzSoEGvqnz5Cho+/GP5+PhkeR2vvjpAoaEh+uefHXrxxe6qXr2G/P0LKzj4qC5duihPT0+NG/eZ5Y4iAAAAwBmQcfJvxilRoqQuXbpIxgEAAACyiHxDvsnrfENHNAAAANitfv2Gmjnzf5ozZ6b27dutsLBTKl26tDp2fFo9erwoX1+/TNdx4sRxRURckpQScv78c3W68/r5+VlCjJubm8aP/1KBgcv1228rFRx8VMnJySpduow6d+6mHj1esusk+80331Ht2nW1ZMlPOnBgrzw8PNWq1UMaMOANder0eJr5AwJK6Pvv52nOnO/1zz87tH37FpUuXUb9+7+h7t17qEuXJ6zm9/Hx1YwZczV37iz988927dq1QwULFlLJkqXUvfvz6tq1u/r1663Q0BDt3v2PGjfO/C6g9Iwa9YmmTPlcYWGndOXKFZ07d1ZVqxqzvLyHh4e++GKqfvttpf744zedOHFcCQmJKlGihJ58srOef/4llStXPtvtAwAAAPIrMk7+zDijR3+iL774TOHhZBwAAAAgq8g35Ju85GLK6YNMccclJSXr2rVbmc/oAG5uripc2FfXr99SYmJynrTBmVFfx6PGjkV9HYv6OlZCQryuXr2ggIDScnV1z+vmOC03N9e7av9t0aKhJGnVqr8twzPnd/mpxubjqmjRUnJ398hw3iJFfGUwuN6hliE/Id84N2rsWNTXsaiv41FjxyHf3Dn56fw7K+62jJOf6mtPvpHIOPcyMo7zor6ORX0djxo7FvV1LDLOnZGfzr+zgnyTM466hkMKAgAAAAAAAAAAAAAAAADkCB3RAAAAAAAAAAAAAAAAAAA5Qkc0AAAAAAAAAAAAAAAAAECOuOV1AwAAAID8KihoV143AQAAAAByDRkHAAAAgLMg3+RPjIgGAAAAAAAAAAAAAAAAAMgROqIBAAAAAAAAAAAAAAAAAHKEjmgAAAAAAAAAAAAAAAAAgByhIxoAAAAAAAAAAAAAAAAAIEfoiAYAAAAAAAAAAAAAAAAAyBE6ogEAAAAAAAAAAAAAAAAAcoSOaAAAAAAAAAAAAAAAAACAHKEjGgAAAAAAAAAAAAAAAAAgR5y2I9qpU6f0zjvv6OGHH1adOnX06KOPavLkyYqJicnxuidOnKhq1arp66+/TneegwcPasCAAWrZsqXq1q2rjh07atasWUpISMjx9gEAAADce8g4AAAAAJwF+QYAAABwTk7ZEe3AgQPq0qWLAgMDVaxYMT300EOKiYnR9OnT9dxzz+nmzZvZXveWLVs0d+7cDOdZv369nnvuOW3YsEEVK1ZUixYtFBERoc8//1yvvfYaQQYAAACAXcg4AAAAAJwF+QYAAABwXm553YDclpiYqKFDhyomJkbjxo1Tt27dJEmxsbF66623tG7dOk2aNEkjR460e93Xrl3T+++/L5PJlO48UVFReueddyRJM2fOVIsWLSRJkZGR6tevn7Zs2aIffvhBffr0yca7AwAAwL3GZDLJxcUlr5uBPETGAQAAgDMh49zbyDcAAABwJuSbtJxuRLTffvtNZ86cUbNmzSwBRpK8vLz06aefysfHR7/88ouioqLsXvfw4cN1/fp11a9fP915FixYoJs3b6pTp06WACNJ/v7+Gj9+vCRp7ty5SkpKsnv7AAAA+cnp0+EaM+Yjde3aUW3aNNdzz3XWjBnf6vbt23atJy4uVq1bN1GLFg3T/efYsSMOehf518WLFzVy5DDt27fH6vVBg/qpRYuGWr/+b8tr48Z9rBYtGmrhwvl3upm4A8g4AAAAdwYZx7Gyk3EWLPjhTjcTDka+AQAAuDPIN45Fvkmf042Itm7dOklSu3bt0kwrXLiwmjRpovXr12vz5s3q2LFjlte7YMECrV+/XkOGDNH169e1Z88em/OZt//oo4+mmVa5cmUZjUaFhITowIEDqlevXpa3DwAAkJ8cPXpYgwcP0O3bMapR437VqFFTBw8e0Pz5c7V1a5CmTZspX1+/LK3r+PHjSkpKUokSJVW3ru3zo4IFC+Vm8+8Kw4e/rZCQYHXq1DWvm4I8RsYBAABwPDKO45FxIJFvAAAA7gTyjeORb9LndB3RQkJCJEnVqlWzOb1KlSpav369jh07luUQc/z4cU2cOFH169fXa6+9pgkTJmQ4ryQZjcZ0tx8SEqJjx44RYgAAwF0pMTFRo0YN1+3bMfrgg4/UsePTklLuihk1ariCgjZpxoxvNXTo+1laX3DwUUnSo48+rtdee91h7b7bJCfbfpTIhx+OUVxcrIoXL36HW4S8QsYBAABwLDLOnUHGgUS+AQAAcDTyzZ1Bvkmf0z2aMyIiQpJUokQJm9MDAgKs5stMXFychg4dKnd3d33++ecyGAzpzhsVFaXY2Nhc3T4AAEB+s3btnzp//pwaNGhsCTCS5OnppWHDRsrb21uBgcsVHR2dpfWFhByTJFWvXtMh7XU2JUuWVIUKFeXj45vXTcEdQsYBAABwLDJO3iLj3FvINwAAAI5Fvslb5BsnHBEtJiZGkuTl5WVzuvl183yZ+eyzzxQSEqKJEyeqbNmyWdq2h4eHXF1t9/Gzd/vpcXPLmz6EBoOr1b+Ru6iv41Fjx6K+jkV9HSs52UWSiyTJxUUy2b6RAZKCgjZJklq3fjjNtEKF/FWvXkNt3bpZO3ZsVbt2j1mmubj8++/U9Q0OTgkxNWrkTojZti1IP/+8UCEhx5ScnKS6deupf/839Pvvgfr55x81duwEPfxwW0nS7NkzNHfuTHXp8ozNu3+6dXtSFy9e0KxZP1iFrJiYW/r118UKCtqk8PAwxcTcko+Pr6pUqaqOHZ9W+/Yd0qzn8uUIrV27RYsX/6zff1+pc+fOydvbS/XqNVDv3q+qSpWqkqQ9e3Zp8OD+lmXN/z116nTVr99Qgwb10759e6zeh5mtGu/fv08///yjDh06oJs3b6hIkaJq2vRBvfTSKypZsmTOip1FBoNLnp2/OoN7IeOQb5wXNXYs6utY1NfxqLHjkG/sQ8bJvxlHSlvfvM445JucuRfyjUTGcVbU17Gor+NRY8eivo5Fxsk68g35xl65nXGcriOawWBQcnKyXMxHSTpMWfhm2rBhg3788Ud16NBBnTp1ynR+c3DJbNtZ3X7623FR4cJ523uyYEHvPN2+s6O+jkeNHYv6Ohb1dYzYWIOuXEn5G05QzNjJk6GSJKOxqs0T08qVK2vr1s06eTLU5vTU9Y2Pj1dY2EkVKuSvvXt3afnyX3Xq1AmZTFLNmvfrxRd7qUmTpllu27x5szV9+rdydXVV3br1VLBgQe3Zs0v9+7+iOnXqWLZvbperq4vl3xmdZKdeJioqSq+99orCwk6paNFiqlOnjgwGN508eUJ79+7W3r27FRFxUS+/3DfNekaNGqZNmzaoRo379eCDzXXo0AFt2LBOO3du19y5P6pChYoqXryY2rd/XNu2bVF0dLQaNWqiIkWKqHjxYnJzc7Wca6Zuk/k18/sx13jBgvn65pspkqRq1arrgQfq6eTJE1qx4letX79Wkyd/rfvvr5Xl+torOdlFrq6uKlTIJ92LDMics2cc8s29gRo7FvV1LOrreNQ495Fv7EPGyb8Z57/1zcuMQ77JHc6eb1K2Q8ZxdtTXsaiv41Fjx6K+jkHGyTryDfkmqxyVcZyuI5qvr68iIyN1+/Ztm9PNwy57e2f8B+DKlSsaNmyYSpUqpdGjR2d521LKUNDJyck276jJ6vYzkpxsUnR0zu7GyS6DwVUFC3orOvq2kpKS86QNzoz6Oh41dizq61jU17Hi4+Msz3NPSkq2fTeNySTl8I7YPOPj8+/tLDl05cplSVLhwsWUmJh2XyxSpJiklMdYpJ7u4pKyH6eub3BwsBITExUVFalPPvlY999fW/XqNVRY2En9888O/fPPDvXvP0gvvtg703YdO3ZE33//nTw8PPX551PUoEEjSVJUVKTef3+otm/fJinl8zW3y/yZJyebbL4Xs9TLzJkzS2Fhp9S8eUuNG/e53NxSTqlNJpN++GGOZs78TosWLdRLL73yn3Ukadeunfr66xl64IH6klLODd98c6AOHTqgn35aoHfeGaZy5Srqo4/Gqnfv5xUdHa2XXnpZ9es3lCQlJiZbfgxP3Sbza6n34T17duubb6bI19dP48d/oXr1GljasnTpIk2e/LmGDXtXP/20VJ6ejrmIkpRkUnJysqKiYnT7dlKG8xYs6M0PCOlw9oxDvnFu1NixqK9jUV/Ho8aOQ76xDxkn/2Yc8+smk7R3b95mHHvyjUTGSY+z5xuJjOPMqK9jUV/Ho8aORX0di4yTdeQb8k1WOeoajtN1RAsICFBkZKQuX76scuXKpZkeERFhmS8j06ZN07Vr11SjRg2NGTPGatrhw4clSX/++afCw8NVuXJlDRgwQH5+fvLz89PNmzd1+fJllShRItvbz0xGB9mdkPqgQe6jvo5HjR2L+joW9XWMpCSTpJQTwfQCjH/HR+X+z4472q7cktC4qSID1+RKkDH/WJze3RGenp5W85mZ62prSOdixYprwoQvrYZOXr16lSZMGKsZM75VrVp1LCf+6Vm2bImSk5P17LPPWwKMlDLU9OjRn+rZZzspMTExa28yAwUKFFDTpg9q4MAhlgAjpdzR0qVLd82c+Z0iIyMVExMjHx8fq2W7d3/e6n14eXnp6ae76NChAwoNPZ7jtqWu8cKFP8hkMql//0FWAUaSunZ9Vtu3b9W2bVv0559/6MknO+V42xlJSso4JCJj90LGyev9g7+tjkeNHYv6Ohb1dTxqnPvIN/Yh4+TfjCP9W9/8knHINzlzL+QbiYzj7KivY1Ffx6PGjkV9HYOMk3XkG/KNvXI74zhdR7Rq1aopJCREoaGhql8/7Y4eGhpqmS8jMf/fU/bo0aM6evSozXlCQkIUEhKixo0ba8CAAZIko9GoPXv2KDQ01GaIyer2AQBAPpSLd9zfzVxdXXPtMRpPPdVZTZs2l5ubQcWKFbea9vjjHRUcfFRLlizS4sU/ZRpi9u7dLUlq3rxVmmklSpRU3br1tHv3P5m2KTO9e6cdrjkuLlZhYWE6dOiA5bWEhHhJ1iGmdu26aZY1v+/07gbPjqSkJEs9GjVqYnOeZs1aaNu2Ldq9+x+HhxjkDBkHAAA4BPnGgoxDxsGdQ74BAAAOQ8aRRL4h3+Q9p+uI1rp1awUGBmrNmjXq3r271bTr169rx44dcnd3V/PmzTNcz4QJEzRhwgSb08aNG6cffvhBgwYN0htvvJFm+3v27NGaNWvSbOPEiRMKCQlRkSJFVLdu2h0YAADkYy4uKXejMKyzfHx8FR0dZXlcxX/FxcVJytpjLFxdXVWyZMl0p7do0VpLlizS0aNHMl3X1atXJCnd9ZUrVyFXQowkRURc0rJlS3TgwD6dPXtG165dlclksgp2tjJcgQIF0rxmMBgkScnJmT/aJatSfz7PPtspw3kvXbqYa9uFY5BxAABAriPf/Gd1ZBwyDu4U8g0AAHAIMk6qVZFvyDd5y+k6orVr106lS5dWUFCQFixYoBdeeEFSyrNbR4wYoZiYGPXo0UPFihWzLJOQkKDTp09LksqXLy93d/dsb79Lly6aOXOmli5dqtatW+uRRx6RJEVGRmr48OGSpFdeeSVH2wAAAHnExUXy9c3rVuS54sWLKzo6SlevXlHp0mXSTL9y5bIkWZ1vZVdAQMrdyekFJnukHoI5q5KT0w5FvGHDWo0e/aESEhJUtGgxVa9eQxUrVlKVKlX1wAP11blzh3TXl9kdSLnF3G4XFxe1a/dYhvOWKJF+iET+QMYBAAAOQb6xIOOQcXDnkG8AAIDDkHEkkW/IN3nP6TqieXl5acKECerXr5/GjBmjpUuXqmzZstq7d68iIiJUs2ZNvfPOO1bLXLp0SR06pOxsa9euVdmyZbO9/YCAAH388cd677339Prrr6tevXoqWrSo/vnnH0VGRqply5bq3bt3Tt4iAABAnqpcuapOnAjVqVMnbQ5TfOrUSct8mZk3b5ZCQ4+rc+duatCgUZrpERGXJP0bZjJSsmQphYeH6fz5c2mGiJakK1ci0rzm6uoqyXZYkaQbN6Kt/v/27duaMGGsEhIS9NZb76pLl+5WwSQ6OirTdt4JhQr5y93dXQkJCXrjjbdUuHCRvG4ScoCMAwAA4FhkHDIO7hzyDQAAgGORb8g3ec01rxvgCE2aNNHixYvVvn17nT9/Xhs2bFCBAgU0cOBAzZ8/X35+fg7d/pNPPqn58+erZcuWCg0N1ZYtW1SiRAkNGzZM06ZN404aAABwV2vaNOXRFRs2rEszLSoqUnv37pK7u3u6z7VP7cyZcG3YsFa//x5oc/rq1askSc2aZfxIDkmW7W3YsDbNtNu3b2v37l1pXjcPPX3t2rU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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ipywidgets import interact\n", "from BY_example_sol import solve_BY_elas\n", "\n", "interact(solve_BY_elas, γ=[('5',5), ('10',10),('15',15),('20',20)],\\\n", " β=[('0.995',0.995),('0.998',0.998), ('0.999',0.999)],\\\n", " ρ=[('2/3', 2./3),('1', 1.0001),('1.5', 1.5),('10', 10)],\\\n", " α=[('0.969',0.969),('0.979',0.979),('0.989',0.989)],\\\n", " ϕ_e=[('0.0002',0.0002),('0.0003432',0.0003432),('0.0004',0.0004)],\\\n", " ν_1=[('0.977',0.977),('0.987',0.987),('0.997',0.997)],\\\n", " σ_w=[('0.03',0.03),('0.0378',0.0378),('0.04',0.04)],\\\n", " μ=[('0.0005', 0.0005),('0.0015', 0.0015),('0.003',0.003)],\\\n", " ϕ_c=[('0.002',0.002),('0.0078',0.0078),('0.01',0.01)]);" ] }, { "cell_type": "markdown", "id": "24afc558", "metadata": {}, "source": [ "# Reference\n", "\n", "[1] Bansal, Ravi, and Amir Yaron. \"Risks for the long run: A potential resolution of asset pricing puzzles.\" The journal of Finance 59, no. 4 (2004): 1481-1509.\n", "\n", "[2] Borovička, Jaroslav, and Lars Peter Hansen. \"Term structure of uncertainty in the macroeconomy.\" In Handbook of Macroeconomics, vol. 2, pp. 1641-1696. Elsevier, 2016.\n", "\n", "[3] Borovička, Jaroslav, and Lars Peter Hansen. \"Examining macroeconomic models through the lens of asset pricing.\" Journal of Econometrics 183, no. 1 (2014): 67-90.\n", "\n", "[4] Lombardo, Giovanni, and Harald Uhlig. \"A theory of pruning.\" International Economic Review 59, no. 4 (2018): 1825-1836." ] }, { "cell_type": "markdown", "id": "4c6fba93", "metadata": {}, "source": [] } ], "metadata": { "interpreter": { "hash": "1c67a9c81f4b1f676ee52b818d3e176be5c9dd44f82a35d6054954eb94c37bc5" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.12" }, "vscode": { "interpreter": { "hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594" } } }, "nbformat": 4, "nbformat_minor": 5 }