![]() Researchers often have to grapple with large data read from and written to a number of different formats, including text files, CSV files, Excel, SQL databases, noSQL databases and proprietary data formats, either local or remote. Any of the others could be the second best, depending on the application and the skill of the programmer. In conclusion, Julia is generally the fastest and requires the least amount of tricky coding to run fast. These findings are in line with results in Arouba and Fernández-Villaverde (2018).ĭetails and code can be found on our web appendix (Aguirre and Danielsson 2020). In this experiment, Julia is the fastest, followed by R, with Matlab again trailing badly. For comparison, we also do a C implementation. For R, we tried both pure R and a C++ implementation (Rcpp). When using Python, we use both pure Python and a version pre-compiled with Numba. Since it is both iterative and dynamic, it captures a large class of numerical problems encountered in practice. The first is an experiment with the GARCH log-likelihood function. To evaluate speed, we also conduct three experiments. The slowest of the four languages is Python, but it does offer an excellent just-in-time compiler, Numba, that can significantly speed up computations where it can be applied (unfortunately, this is only in very simple calculations). Julia was designed with speed in mind, taking advantage of modern compiler techniques, and is generally the fastest of the four.Ĭonsequently, it doesn’t require the programmer to use complicated techniques for speeding, called code up, resulting in Julia’s code being both more readable and faster. When ranking the inherent suitability of the four languages for numerical computations, we consider Julia the best, followed by R, then Matlab, with Python the worst. One advantage is that it allows Unicode in equations, allowing Greek letters and other characters to be used in calculations. Not surprisingly, it has been adopted in high quality projects, such as Perla et al. It doesn’t have any historical baggage, and as a result, the code is clean, fast and less error-prone than the others. Julia is the newcomer at only eight years old, and it shows. While in Matlab and Julia the line is simply: For example, suppose you have two matrices, X and Y, and want to multiply them into each other. The main numerical and data libraries have been clumsily grafted on it, so it is unnatural, hard to work with, and prone to errors which are hard to diagnose. However, it is not a good language for general numerical programming. It is also really good at interacting with external libraries – the reason it is widely used in machine learning. Python, unlike the other three, started out as a general-purpose programming language used for file management and text processing. ![]() ![]() Similar to Matlab, it is hampered by poor design, but the richness of its libraries make it perhaps the most useful of the four today. It was initially conceived as a language for statistical computing and data visualizations. R, in the form of its precursor SPlus, also dates back to the 1970s. ![]() But while it does slowly add new features, it is still held back by poor design choices. Matlab dates back almost half a century and has been a reliable workhorse for economic researchers ever since. We have two additional criteria common in data science: importing a very large dataset and a computationally intensive subroutine. ![]() To narrow the question down, we have three separate criteria in mind, all drawn from our work.įirst, one of us has written a book called Financial Risk Forecasting (Danielsson 2011), accompanied by practical implementation in all of the four languages, which provides the ideal test case for the power of libraries available for researchers. There is, of course, no single way to answer the question - depending on the project, any of the four could be the best choice. With all the developments since then, is R still in the lead? When we looked at this last time here on Vo圎U (Danielsson and Fan 2018) two years ago, we concluded that R was the best in most cases. While a large number of general-purpose programming languages are used in economic research, we suspect the four most common are Julia, R, Matlab, and Python. ![]()
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