kozjuli/quantecon_nyu_2016

Quantitative Economics

Topics in Computational Economics

John Stachurski

This is the home page of ECON-GA 3002, a PhD level course on computational economics to be held at NYU in the spring semester of 2016.

(Note: This document is preliminary and still under development)

Semi-Random quote

All this technology carries risk. There is no faster way for a trading firm to destroy itself than to deploy a piece of trading software that makes a bad decision over and over in a tight loop. Part of Jane Street's reaction to these technological risks was to put a very strong focus on building software that was easily understood--software that was readable.

-- Yaron Minsky, Jane Street

Table of Contents:

• News
• References
• Prerequisites
• Syllabus
  • Part I: Programming
  • Part II: Comp Econ Foundations
  • Part III: Applications
• Assessment
• Additional Resources

News

New Location!

Please note that the lecture room has changed to room 5-75 in the Stern Building.

The time is unchanged: Friday 9am--11am

Please be sure to bring your laptop

References

• http://quant-econ.net/
• Secondary / Useful / Related / Recommended texts
  • Kendall Atkinson and Weimin Han (2009). Theoretical Numerical Analysis (3rd ed)
  • Ward Cheney (2001). Analysis for Applied Mathematics
  • Nancy Stokey and Robert Lucas Jr. (1989) Recursive Methods in Economic Dynamics
  • John Stachurski (2009). Economic Dynamics: Theory and Computation

Prerequisites

I assume that you have

• At least a bit of programming experience
  • E.g., some experience writing Matlab code or similar
• Econ PhD level quantitative skills, including some familiarity with
  • Linear algebra
  • Basic analysis (sequences, limits, continuity, etc.)
  • Dynamics (diff equations, finite Markov chains, AR(1) processes, etc.)

If you would like to prepare for the course before hand please consider

• Installing Linux on a VM or in a bootable partition on your laptop
  • Backup your data first!
  • Help available in the first class
• Build up your Linux skills (and profit)
• Do some exercises in real analysis if you are rusty
  • These notes look like about the right level
• Read the first 3 chapters of RMT if you don't know any Markov chain theory or dynamic programming

Syllabus

Below is a sketch of the syllabus for the course. The details are still subject to some change.

Part I: Programming

Introduction

• Scientific programming environments --- what do we want?
  • Speed?
  • Productivity?
  • Fun?
• Why Python? And what is it anyway?
  • Background
    • http://quant-econ.net/py/about_py.html
    • http://www.galvanize.com/blog/2015/10/01/bill-and-melinda-gates-foundation-saves-lives-with-python/
  • Philosophy
    • http://legacy.python.org/dev/peps/pep-0020/
    • https://gist.github.com/sloria/7001839
  • The second best language for everything
    • https://github.com/jstac/backup_scripts
• What's Julia?
  • http://julialang.org/blog/2012/02/why-we-created-julia/
  • http://libertystreeteconomics.newyorkfed.org/2015/12/the-frbny-dsge-model-meets-julia.html
• Open Source
  • Examples of how contributions improve on the standard library
    • http://docs.python-requests.org/en/latest/
    • https://python-programming.courses/general/better-date-and-time-handling-with-arrow/
  • Open science
    • http://www.openscience.org/blog/?p=269
    • https://opensource.com/resources/open-science
    • http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261
    • http://devblogs.nvidia.com/parallelforall/open-reproducible-computational-chemistry-python-cuda/
  • How can open source produce better software than firms acting alone?
    • https://github.com/
    • https://www.moore.org/newsroom/press-releases/2015/07/07/$6m-for-uc-berkeley-and-cal-poly-to-expand-and-enhance-open-source-software-for-scientific-computing-and-data-science
    • https://www.continuum.io/

Coding Foundations

• UNIX and the UNIX shell
  • http://swcarpentry.github.io/shell-novice/
• Editing = Vim
  • https://danielmiessler.com/study/vim/
  • https://realpython.com/blog/python/vim-and-python-a-match-made-in-heaven/
  • http://vim-adventures.com/
  • http://www.openvim.com/
• Tmux
  • http://tangosource.com/blog/a-tmux-crash-course-tips-and-tweaks/
• Version control
  • https://github.com/swcarpentry/git-novice
  • http://gitimmersion.com/
  • http://luisbg.blogalia.com//historias/76017 --- Git cheatsheet
• General software engineering skills
  • http://software-carpentry.org/
• Speed and Efficiency
  • Hardware
  • Interpreted / JIT compiled / AOT compiled
  • Vectorized code
• C and Fortran
  • GSL
  • http://computationalmodelling.bitbucket.org/tools/FORTRAN.html
• Test driven development:
  • http://code.tutsplus.com/tutorials/beginning-test-driven-development-in-python--net-30137

Core Python

• Getting started
  • The REPLs: Python and IPython shells
  • Jupyter
  • The beauty of introspection on the fly
• Basic syntax
  • http://quant-econ.net/py/python_by_example.html
  • http://quant-econ.net/py/python_essentials.html
• OOP. It's like structs with lazy evaluation
  • http://quant-econ.net/py/python_oop.html
  • http://quant-econ.net/py/python_foundations.html
  • http://quant-econ.net/py/python_advanced_features.html
• Python style
  • https://blog.hartleybrody.com/python-style-guide/
  • https://google.github.io/styleguide/pyguide.html
  • https://www.python.org/dev/peps/pep-0008/
• Other general Python resources
  • https://leanpub.com/intermediatepython/read
  • http://nbviewer.ipython.org/github/rajathkumarmp/Python-Lectures/blob/master/01.ipynb
  • http://book.pythontips.com/en/latest/
• Debugging
  • http://www.scipy-lectures.org/advanced/debugging/

Scientific Python I: SciPy and Friends

• General Resources
  • https://github.com/jrjohansson/scientific-python-lectures
  • http://bender.astro.sunysb.edu/classes/python-science/
  • http://computationalmodelling.bitbucket.org/tools/
• NumPy and SciPy
  • http://quant-econ.net/py/numpy.html
  • http://quant-econ.net/py/scipy.html
• Jupyter
  • http://nbviewer.ipython.org/
  • http://jupyter.cs.brynmawr.edu/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb
  • http://mindtrove.info/#nb-extensions
  • https://plot.ly/ipython-notebooks/ipython-notebook-tutorial/
  • https://github.com/nicolaskruchten/pyconca/blob/master/jupyter_magic.ipynb
• Matplotlib
  • http://quant-econ.net/py/matplotlib.html
  • http://nbviewer.ipython.org/github/clbarnes/plotstyles/blob/master/plotstyles.ipynb

Scientific Python II: The Ecosystem

• Pandas
  • http://geoffboeing.com/2015/11/landscape-us-rents/
• Numba and other JIT compilers
  • http://blog.pyston.org/2015/11/03/102/
  • http://nbviewer.ipython.org/github/postelrich/fin_examples/blob/master/cva/cva1.ipynb
  • https://www.ibm.com/developerworks/community/blogs/jfp/entry/A_Comparison_Of_C_Julia_Python_Numba_Cython_Scipy_and_BLAS_on_LU_Factorization?lang=en
• AOT compilers
  • Cython
  • Others (Nuitka?)
• Visualization
  • Plotly, Bokeh
• Statistics and machine learning
  • https://www.youtube.com/watch?v=5W715nfJNJw
  • PyMC, Statsmodels
  • http://scikit-learn.org/stable/related_projects.html
  • https://www.youtube.com/watch?v=L7R4HUQ-eQ0&feature=youtu.be
  • Seaborn
• Parallel processing
  • http://www.admin-magazine.com/HPC/Articles/Parallel-Python-with-Joblib
  • http://www.davekuhlman.org/python_multiprocessing_01.html
  • http://ufora.github.io/ufora/
• Blaze
• Wrappers
  • https://github.com/wjakob/pybind11
  • f2py and related solutions (https://www.euroscipy.org/2015/schedule/presentation/58/)
• NetworkX
• Sympy
  • http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/SymPy.ipynb
• Webscraping
  • http://shop.oreilly.com/product/0636920034391.do
  • http://robertwdempsey.com/simple-python-web-scraper-get-pricing-data/

Julia

• General
  • http://julialang.org/
  • http://www.slideshare.net/acidflask/an-introduction-to-julia
  • http://doodlingindata.com/2015/08/11/writing-good-julia-functions/
  • http://computationalmodelling.bitbucket.org/tools/Julia.html
• Libraries
  • QuantEcon.jl
  • Distributions.jl
  • Gadfly

Part II: Comp Econ Foundations

Markov Dynamics I: Finite State

• Asymptotics
• The Dobrushin coefficient
• A simple coupling argument
• Code from QuantEcon
• Applications

Functional Analysis

• A dash of measure and integration
• Metric / Banach / Hilbert space
  • Space of bounded functions (cbS is a closed subset)
  • The Lp spaces
• Banach contraction mapping theorem
  • Blackwell's sufficient condition
• Orthogonal projections
• Neumann series lemma
• Applications
  • The Lucas 78 asset pricing paper

Markov Dynamics II: General State

• General state spaces
  • Feller chains, Boundedness in prob
  • Monotone methods
• LLN and CLT
• Look ahead method
  • examples in lae_extension?
  • examples in poverty traps survey?
• Applications
  • ARCH, AZ, STAR, MCMC, etc.

Solving Forward Looking Models

• L2 methods
• Asset Pricing

Dynamic Programming

• Fundamental theory
  • The principle of optimality
  • VFI
  • Howard's policy iteration algorithm
• Approximation
  • Preserving the contraction property
  • MC for integrals
• Weighted sup norm approach

Part III: Applications

DP II: Applications and Extensions

• The Coleman operator
  • The income fluctuation problem
  • Benhabib wealth distribution paper, heavy tails
• Recursive and risk sensitive preferences
  • Stochastic Optimal Growth Model with Risk Sensitive Preferences
• Other (see TE paper, monotone LLN)

Optimal Stopping

• Reservation rule operator
  • Theory
  • Applications

Coase's Theory of the Firm

• Theory
• Implementation

Assessment

See lecture 1 slides.

Notes

A completed class project is a GitHub repository containing

• Code
• A Jupyter notebook that pulls all the code together and runs it
• A PDF document that provides analysis and reports results
  • like a short research paper

Good projects demonstrate proficiency with

• good programming style
• techical material discussed in the coure

Additional Resources

• Jupyter

  • https://github.com/bloomberg/bqplot
  • https://cloud.google.com/datalab/
  • http://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/
  • https://github.com/jupyter/jupyterhub
  • http://mybinder.org/

• Data, machine learning and prediction

  • www.galvanize.com/blog/how-random-forest-modeling-solves-seattles-bikesharing-problem/
  • https://anaconda.org/ikkebr/brazilian-federal-payroll/notebook

• Language comparisons

  • http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html
  • http://scottsievert.github.io/blog/2015/09/01/matlab-to-python/
  • https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Meets_Julia_Micro_Performance?lang=en

• Python, general

  • https://www.reddit.com/r/Python/comments/3s4j6n/zen_of_python_verse_2/
  • https://github.com/s16h/py-must-watch
  • https://www.reddit.com/r/Python/comments/3m3ll9/where_python_is_used_in_industry_other_than_webdev/
  • http://bruceeckel.github.io/2015/08/29/what-i-do/
  • http://blog.apcelent.com/python-decorator-tutorial-with-example.html
  • http://noeticforce.com/best-free-tutorials-to-learn-python-pdfs-ebooks-online-interactive

Vectorization: * http://blog.datascience.com/straightening-loops-how-to-vectorize-data-aggregation-with-pandas-and-numpy/

Good reads * http://undsci.berkeley.edu/article/cold_fusion_01 * https://msdn.microsoft.com/en-us/library/dn568100.aspx