Economic research strongly depends upon the economist’s ability to identify relevant information for causal inference and forecast accuracy efficiently. We address this goal in our Julia’s ParallelGSReg project, developing different econometric-machine learning packages. In JuliaCon 2023, we will present an improved version of our dimensionality reduction package (including non-linear algorithms) and a new "research acceleration package" with automatized Latex code and AI-bibliographic features.
In their recent volume of “Econometrics with Machine Learning”, Chan & Mátyás (2022) remind us of the well-established distinction in which Econometrics and Machine Learning are perceived as alternative methodological cultures: one focused on prediction (model selection, sampling properties, accuracy metrics) and the other on explanation (causal inference, hypothesis testing, coefficient robustness). Moving away from this false dichotomy, we introduce ParallelGSReg (https://github.com/ParallelGSReg): a Julia’s research project with several packages (GlobalSearchRegression.jl, GlobalSearchRegressionGUI.jl and ModelSelection.jl) aimed at 1) building bridges between those complementary cultures; and 2) encouraging economic researchers to use Julia in order to improve computational efficiency in model selection tasks (particularly those using dimensionality reduction techniques with causal-inference requirements). In JuliaCon 2018, the focus was on “efficiency”. We presented the world-fastest all-subset-regression command (GlobalSearchRegression.jl, which runs up to 3165 times faster than the original Stata-code and up to 197 times faster than well-known R-alternatives; see https://github.com/ParallelGSReg/JuliaCon2019/blob/master/GlobalSearchRegression.jl-paper.pdf). In 2019, the goal was “ease of use” for what we improved our Graphic-User-Interface (GlobalSearchRegressionGUI.jl) and developed a basic package (ModelSelection.jl) to automatize Julia-to-Latex migration of dimensionality reduction results (which also includes all GlobalSearchRegression.jl functions and additional features like regularization and cross-fold validation). For JuliaCon 2023 the target is “scope and integration”, for which we are: