🤖 AI Summary
This study addresses the high computational costs and reproducibility challenges prevalent in financial econometrics research. To overcome these barriers, we propose an open-source, browser-based AI-augmented research engine that dynamically schedules computationally intensive tasks to hardware best suited for execution. The embedded AI assists solely in method selection and interpretation of results—never in data generation—ensuring users can reproduce empirical analyses using code identical to that in published papers. The system integrates established financial econometric techniques (notably return-based approaches for handling non-stationary price series), an AI-driven interpretability module, and a cloud-native reproducible architecture. It currently implements 17 validated econometric models, delivers real-time parameter estimates, provides public reproducibility endpoints, and has successfully replicated key findings from seminal studies on financial contagion.
📝 Abstract
Turning a promising economic idea into a credible empirical finding is, in practice, an expensive undertaking: it demands a great deal of specialised computation, and the results are seldom released in a form that others can check or build upon. Econstellar is our response. It is an open, publicly served research engine that runs publication-grade financial econometrics from an ordinary web browser and explains what the results mean, so that a reader does not merely read a finding but can re-run it, vary its inputs, and trace exactly how it was produced. Three choices give the system its character. The heavy computation is placed on the processor that suits it, rather than forced onto hardware ill-matched to the task, which is much of the reason analysis of this kind is so rarely served to the public. An artificial-intelligence assistant selects and interprets the analyses but never originates a number, so every quantity it reports is a real computation the reader can reproduce. And the engine a visitor exercises is the same code that produced the figures in our published research. We expose seventeen econometric methods, each reported with a verified live value and reproducible at the public endpoint, computed under a single discipline: prices are treated as non-stationary and all methods are applied to returns. The system also regenerates, on demand, the headline result of an accompanying study of financial contagion, from the package that generated it. The platform is the working core of an active research programme spanning three software releases and three preprints, and it is available now, free and open-source, at a live public address. Our aim is a simple one: to shorten the distance between a research claim and the moment another person can independently verify it.