Jupyter (formerly iPython) notebook:
The Python code in the Solow growth model notebooks that are the lecture notes is static: it has been executed. But the best way to understand what is going on in the Python code—in the Solow growth model—is for you to play with the code and so conduct what-if simulation experiments with the model yourself. In the last cell of each notebnook there is a datahub link, something like http://datahub.berkeley.edu/user-redirect/interact?account=braddelong&repo=long-form-drafts&branch=master&path=solow-model-4-using.ipynb. Clicking on that link should send you to a Jupyter Notebook server authenticated by your CalNet account in which you can edit and play with the Python code—and thus with the model descriptions and simulations—to gain a deeper and better kind of knowledge. I strongly encourage you to do so.
#berkeley #economicgrowth #teaching #2020-01-18