./ddp_ex_job_search_py.ipynb:    "The following paramter values are from [lakemodel_example.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/examples/lakemodel_example.py)."
./ddp_ex_job_search_py.ipynb:    "mimicking [lakemodel_example.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/examples/lakemodel_example.py)."
./recursive_repeated_games.ipynb:    "The stage game is simply created by `NormalFormGame((A, B))`. This command takes a tuple of players (in this case `A` and `B`) and using the information held about payoffs for each player creates a normal form game. For more information consider reviewing the [Tools for Game Theory notebook](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/game_theory_jl.ipynb).\n",
./ddp_ex_optgrowth_py.ipynb:    "the [solution notebook](http://nbviewer.ipython.org/github/QuantEcon/QuantEcon.py/blob/master/solutions/discrete_dp_solutions.ipynb)\n",
./ddp_ex_MF_7_6_2_jl.ipynb:    "`Julia` translation of the [Python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/ddp_ex_MF_7_6_2_py.ipynb)"
./ddp_ex_MF_7_6_3_jl.ipynb:    "`Julia` translation of the [Python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/ddp_ex_MF_7_6_3_py.ipynb)"
./ddp_ex_MF_7_6_3_jl.ipynb:       "// https://github.com/JuliaLang/Interact.jl/blob/master/src/IJulia/ijulia.js\r\n",
./permanent_income.ipynb:    "with $\\beta R = 1$ into a linear state space system, applies two different approaches to solving the model and compares outcomes from those two approaches. After confirming that answers produced by the two methods agree, it applies the quantecon [LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class to illustrate various features of the model. \n",
./game_theory_jl.ipynb:    "* [A Recursive Formulation of Repeated Games](https://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/recursive_repeated_games.ipynb)"
./ddp_ex_MF_7_6_5_jl.ipynb:    "Julia translation of the [Python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/ddp_ex_MF_7_6_5_py.ipynb)"
./ddp_ex_MF_7_6_5_jl.ipynb:       "// https://github.com/JuliaLang/Interact.jl/blob/master/src/IJulia/ijulia.js\n",
./ddp_ex_MF_7_6_4_jl.ipynb:    "Julia translation of the [python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/ddp_ex_MF_7_6_4_py.ipynb)"
./samuelson.ipynb:    "[LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class to do\n",
./samuelson.ipynb:    "[LinearStateSpace](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/lss.py) class"
./rmt3_ch11.ipynb:    "url = \"https://raw.githubusercontent.com/EconForge/dolo/master/examples/models/rmt3_ch11.yaml\"\n",
./markov_chain_ex01_jl.ipynb:    "Julia translation of the [Python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/markov_chain_ex01_py.ipynb), translated by **Ryumei Nakada**"
./markov_chain_ex01_jl.ipynb:       "// https://github.com/JuliaLang/Interact.jl/blob/master/src/IJulia/ijulia.js\n",
./lucas_asset_pricing_model.ipynb:    "This note describes why and how we modified the computer code of the original <a href=\"https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/models/lucastree.py\">lucastree.py</a> module. We briefly reformulate Lucas' asset pricing problem as found in the  <a href=\"http://quant-econ.net/py/lucas_model.html\">lecture notes</a> . Denote by $y$ the fruit of the tree. The fruit’s growth rate follows the process $G(y,z') = y^\\alpha z'$ with $z' \\sim \\log N(0,\\sigma^2)$. The investor has CRRA preferences with curvature parameter $\\gamma$ and discount factor $\\beta$. Following <a href=\"https://www.jstor.org/stable/1913837?seq=1#page_scan_tab_contents\">Lucas (1978)</a> , the pricing function, $p(y)$, solves the functional equation:\n",
./lucas_asset_pricing_model.ipynb:    "    https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/models/lucastree.py\n",
./IntroToStan_basics_workflow.ipynb:    "It is worthwhile to build each layer of complexity on a different feature [git branch](https://www.atlassian.com/git/tutorials/). This minimises the possibility of contamination between models. Once the more complex model is working fine, then merge it back into the master branch. \n",
./ddp_ex_MF_7_6_1_jl.ipynb:    "Julia translation of the [Python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/ddp_ex_MF_7_6_1_py.ipynb)"
./ddp_ex_MF_7_6_1_jl.ipynb:       "// https://github.com/JuliaLang/Interact.jl/blob/master/src/IJulia/ijulia.js\r\n",
./dataframes_examples.ipynb:    "2. `:left`: The output contains rows for values of the key that exist in the first (left) argument to join, whether or not that value exists in the second (right) argument *(this is the `keepmaster)` option in Stata)*\n",
./dataframes_examples.ipynb:    "5. `:semi`: Like an inner join, but output is restricted to columns from the first (left) argument to join *(this is the `keep(match master)` option in Stata)*\n",
./NKModel_CLMM_matlab.ipynb:     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
./ddp_ex_job_search_jl.ipynb:    "The following paramter values are from [lakemodel_example.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/examples/lakemodel_example.py)."
./ddp_ex_job_search_jl.ipynb:    "mimicking [lakemodel_example.py](https://github.com/QuantEcon/QuantEcon.py/blob/master/examples/lakemodel_example.py)."
./ddp_ex_MF_7_6_6_jl.ipynb:    "`Julia` translation of the [python version](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/ddp_ex_MF_7_6_6_py.ipynb)"

README.md
*.ipynb
dependencies/