ParallelGSReg/GlobalSearchRegression.jl: v1.0.5

GlobalSearchRegression is both the world-fastest all-subset-regression command (a widespread tool for automatic model/feature selection) and a first-step to develop a coeherent framework to merge Machine Learning and Econometric algorithms. Written in Julia, it is a High Performance Computing version of the Stata-gsreg command (get the original code here). In a multicore personal computer (we use a Threadripper 1950x build for benchmarks), it runs up-to 100 times faster than the original Stata-code and up-to 10 times faster than well-known R-alternatives (pdredge). Notwithstanding, GlobalSearchRegression main focus is not only on execution-times but also on progressively combining Machine Learning algorithms with Econometric diagnosis tools into a friendly Graphical User Interface (GUI) to simplify embarrassingly parallel quantitative-research. In a Machine Learning environment (e.g. problems focusing on predictive analysis / forecasting accuracy) there is an increasing universe of "training/test" algorithms (many of them showing very interesting performance in Julia) to compare alternative results and find-out a suitable model. However, problems focusing on causal inference require five important econometric features: 1) Parsimony (to avoid very large atheoretical models); 2) Interpretability (for causal inference, rejecting "intuition-loss" transformation and/or complex combinations); 3) Across-models sensitivity analysis (uncertainty is the only certainty; parameter distributions are preferred against "best-model" unique results); 4) Robustness to time series and panel data information (preventing the use of raw bootstrapping or random subsample selection for training and test sets); and 5) advanced residual properties (e.g. going beyond the i.i.d assumption and looking for additional panel structure properties -for each model being evaluated-, which force a departure from many traditional machine learning algorithms). For all these reasons, researchers increasingly prefer advanced all-subset-regression approaches, choosing among alternative models by means of in-sample and/or out-of-sample criteria, model averaging results, bayesian priors for theoretical bounds on covariates coefficients and different residual constraints. While still unfeasible for large problems (choosing among hundreds of covariates), hardware and software innovations allow researchers to implement this approach in many different scientific projects, choosing among one billion models in a few hours using standard personal computers.