Here are some useful python codes I use for my research.
1. BLP estimation: Codes to estimate Random Coefficients Logit demand system. This is a python implementation of Simulated method of moments (SMM) with the nested contraction mapping to find mean utility.
2. Applied General Equilibrium Model: An example of solving a system of 16 coupled nonlinear equations in economics.
3. Scalable Parallel Computation of Monte Carlo simulations: An example of using mpi4py (python implementation of openmpi) to speed up independent simulations. I run multiple instances on sharcnet (128 cores at the same time) which speed up the project that would normally requires a few months of computations (on my 2-core laptop ) to a few days.
4. Scraping dynamically generated web content with selenium: The advantage of selenium is that since it is a complete web browser automation framework, so theoretically anything you can render on your web browser can be scrapped.
1. BLP estimation: Codes to estimate Random Coefficients Logit demand system. This is a python implementation of Simulated method of moments (SMM) with the nested contraction mapping to find mean utility.
2. Applied General Equilibrium Model: An example of solving a system of 16 coupled nonlinear equations in economics.
3. Scalable Parallel Computation of Monte Carlo simulations: An example of using mpi4py (python implementation of openmpi) to speed up independent simulations. I run multiple instances on sharcnet (128 cores at the same time) which speed up the project that would normally requires a few months of computations (on my 2-core laptop ) to a few days.
4. Scraping dynamically generated web content with selenium: The advantage of selenium is that since it is a complete web browser automation framework, so theoretically anything you can render on your web browser can be scrapped.