When they existed, it was often unclear which package to use and how to use it. With Julia, it was harder to find off-the-shelf libraries. Some of the available library code was a bit dodgy, like GARCH estimation which had convergence issues, and there was no code for multivariate GARCH or more fancy specifications. We could do most things in Python using NumPy (numerical Python), but it was not trouble-free. We have built much larger projects with both, never running into any serious language limitations. All required functionality was available, either through built-in methods or from outside libraries. The published book and the accompanying website used R and MATLAB. Implementing the Financial Risk Forecasting algorithms Our starting criteria is how easy it was to implement the algorithms in Financial Risk Forecasting, followed by six others. The other has recently translated all that code into Julia and Python, all downloadable. One of us has written a book called Financial Risk Forecasting, where risk forecasting methods are implemented in MATLAB and R. That said, we have specific criteria in mind. This is of course highly subjective - depending on the objective, any of these four could be the best choice. This naturally invites the question: which of these is the best? We suspect the most common are MATLAB, Python and R, with Julia increasingly used, helped by Thomas Sargent's endorsement. A large number of general-purpose numerical programming languages are used by economic researchers.
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