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QuantEcon
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auto publishing updates to notebooks
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about_lectures.ipynb

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}
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],
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"metadata": {
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"date": 1601775348.0858822,
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"date": 1603747459.4914663,
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"filename": "about_lectures.rst",
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"kernelspec": {
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"display_name": "Python",

aiyagari.ipynb

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}
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"metadata": {
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"date": 1603747459.544695,
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"filename": "aiyagari.rst",
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"kernelspec": {
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"display_name": "Python",

ar1_processes.ipynb

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}
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"metadata": {
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"date": 1603747459.6796107,
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"filename": "ar1_processes.rst",
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"kernelspec": {
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cake_eating_numerical.ipynb

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"\n",
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"1. Take an arbitary intial guess of $ v $. \n",
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"1. Obtain an update $ w $ defined by \n",
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" \n",
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" $$\n",
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" w(x) = \\max_{0\\leq c \\leq x} \\{u(c) + \\beta v(x-c)\\}\n",
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" $$\n",
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" \n",
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"1. Stop if $ w $ is approximately equal to $ v $, otherwise set\n",
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" $ v=w $ and go back to step 2. \n",
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"\n",
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}
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],
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"metadata": {
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"date": 1603747459.85727,
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"filename": "cake_eating_numerical.rst",
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"kernelspec": {
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cake_eating_problem.ipynb

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}
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"metadata": {
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"date": 1603747460.0518348,
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"filename": "cake_eating_problem.rst",
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"kernelspec": {
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"display_name": "Python",

career.ipynb

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"\n",
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"For workers, wages can be decomposed into the contribution of job and career\n",
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"\n",
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"- $ w_t = \\theta_t + \\epsilon_t $, where \n",
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" \n",
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" - $ \\theta_t $ is the contribution of career at time $ t $ \n",
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" - $ \\epsilon_t $ is the contribution of the job at time $ t $ \n",
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" \n",
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"- $ w_t = \\theta_t + \\epsilon_t $, where\n",
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" * $ \\theta_t $ is the contribution of career at time $ t $\n",
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" * $ \\epsilon_t $ is the contribution of the job at time $ t $ \n",
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"\n",
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"\n",
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"At the start of time $ t $, a worker has the following options\n",
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}
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],
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"metadata": {
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"date": 1603747460.1728625,
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"filename": "career.rst",
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"kernelspec": {
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"display_name": "Python",

cass_koopmans_1.ipynb

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}
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"metadata": {
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"date": 1603747460.300955,
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"filename": "cass_koopmans_1.rst",
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cass_koopmans_2.ipynb

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" problem and the Hicks-Arrow prices. \n",
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"- A **Big** $ K $ **, little** $ k $ trick widely used in\n",
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" macroeconomic dynamics. \n",
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"\n",
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"\n",
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"> - We shall encounter this trick in [this lecture](https://lectures.quantecon.org/py/rational_expectations.html#)\n",
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" and also in [this lecture](https://lectures.quantecon.org/py/dyn_stack.html#). \n",
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"\n",
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"\n",
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"\n",
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" - We shall encounter this trick in [this lecture](https://lectures.quantecon.org/py/rational_expectations.html#)\n",
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" and also in [this lecture](https://lectures.quantecon.org/py/dyn_stack.html#). \n",
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"- A non-stochastic version of a theory of the **term structure of\n",
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" interest rates**. \n",
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"- An intimate connection between the cases for the optimality of two\n",
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" competing visions of good ways to organize an economy, namely: \n",
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"\n",
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"\n",
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"> - **socialism** in which a central planner commands the\n",
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" allocation of resources, and \n",
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"- **capitalism** (also known as **a market economy**) in\n",
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" which competitive equilibrium **prices** induce individual\n",
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" consumers and producers to choose a socially optimal allocation\n",
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" as an unintended consequence of their selfish\n",
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" decisions \n",
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"\n",
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" - **socialism** in which a central planner commands the\n",
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" allocation of resources, and \n",
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" - **capitalism** (also known as **a market economy**) in\n",
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" which competitive equilibrium **prices** induce individual\n",
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" consumers and producers to choose a socially optimal allocation\n",
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" as an unintended consequence of their selfish\n",
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" decisions \n",
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"\n",
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"\n",
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"Let’s start with some standard imports:"
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"Combining [(12)](#equation-cond4) and [(2)](#equation-eq-price), we get:\n",
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"\n",
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"$$\n",
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"- \\beta^{T+1} \\mu_{T+1} \\leq 0\n",
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"-\\beta^{T+1} \\mu_{T+1} \\leq 0\n",
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"$$\n",
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"\n",
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"Dividing both sides by $ \\beta^{T+1} $ gives\n",
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}
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],
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"metadata": {
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"date": 1603747460.4534752,
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"filename": "cass_koopmans_2.rst",
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"kernelspec": {
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"display_name": "Python",

coleman_policy_iter.ipynb

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}
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"metadata": {
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"date": 1603747460.7550678,
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"filename": "coleman_policy_iter.rst",
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"kernelspec": {
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complex_and_trig.ipynb

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}
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"date": 1603747460.9015875,
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"filename": "complex_and_trig.rst",
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"kernelspec": {
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"display_name": "Python",

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