EcoNumericalMethod
Introduction
The code for Numerical Method in Economics in Zhejiang University (Instructor: Prof. Eric R. Young).
The source code in the ./src/ fodler, and ./figure/ include the figures coming from the code.
Of course, all of this code is based on the MATLAB code given by the Prof. Eric R. Young and is translated into Python code by me, as my PC is inconvenient to use MATLAB (oh, MATLAB is too heavy...) and I am more familiar with Python.
Code
- zje_projection.py: using projection to solve growth model
- zje_projection_2.py: using projection to solve growth model
- zje_projection_pchip.py: using projection to solve growth model
- zje_dp_pchip.py: dynamic programming with pchip
- zje_dp_pchip2.py: dynamic programming with pchip
- zje_dp_discrete.py: discrete dynamic programming
- growth_model_zje_run.py: using first-, second- and third- order solutions to solve growth model (Now, I only finish the first-order solution, and I will finish the remain part in the future.)
- LQ1.py: compute deterministic LQ problem and compute Euler equation errors <<<<<<< HEAD
More details
As we know, MATLAB is quite suitable for the matrix operations, symbolic operations and optimization. So, when I use Python, first I have to find some simple but powerful packages to do these operations. Of course, Python does have these functions.
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For matrix operations, I use NumPy.
NumPy is the fundamental package for scientific computing with Python. It contains among other things:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
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For symbolic operations, SymPy is quite powerful.
SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python.
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For optimization, SciPy is my first choice.
… including signal processing, optimization, statistics and much more.