Working Paper: NBER ID: w20263
Authors: S. Boraan Aruoba; Jess Fernández-Villaverde
Abstract: We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++11, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration with grid search, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and briefly comment on the strengths and weaknesses of each language.
Keywords: programming languages; economics; stochastic neoclassical growth model; execution times
JEL Codes: C0; E0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| C (Y60) | Execution Speed (C69) |
| Fortran (C88) | Execution Speed (C69) |
| Julia (Y70) | Execution Speed (C69) |
| Python (C88) | Execution Speed (C69) |
| MATLAB (C88) | Execution Speed (C69) |
| R (C29) | Execution Speed (C69) |
| Mathematica (C88) | Execution Speed (C69) |