This post is inspired by Frank Diebold’s
Albert Alex Zevelev
My interests include Household Finance, Housing, and Macro Finance. I work on methods that exploit non-causal machine learning techniques to improve causal inference.
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This post compares the way random variables are handled in Julia/MATLAB/R/STATA/Mathematica/Python. It was inspired by Bruce Hansen’s recent textbook which compares statistical commands in Matlab/R/STATA on page 114. This post will focus on the main methods for working with random variables in a language: e.g. Distributions.jl is the flagship Julia package for random variables, MATLAB’s internal distributions, Base R, Base STATA, Mathematica, and Python’s SciPy.
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