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.

翻译：摘要：在实践中，将富有前景的经济学构想转化为可信的经验发现，是一项成本高昂的事业：这需要大量专业化计算，而结果往往难以以可供他人检验或在此基础上拓展的形式发布。Econstellar正是我们对此的回应。它是一个开放、公共部署的研究引擎，用户通过普通网页浏览器即可运行达到发表水平的金融计量经济学分析，并获取结果解读——读者不仅能阅读研究发现，更能复现分析、调整输入参数，并精确追踪结果的生成过程。该系统的设计基于三项核心选择：将繁重计算部署于最适配的处理器，而非强加于不匹配的硬件——这正是此类分析极少面向公众开放的主要原因；人工智能助手负责选择与解读分析，但绝不生成任何数据，因此其报告的所有数值均为读者可复现的真实计算结果；访问者使用的引擎与生成我们已发表研究中图表的是同一套代码。我们开放了十七种计量经济学方法，每项方法均附带可验证的实时数值，并在公共端点可复现——所有计算遵循统一准则：将价格视为非平稳序列，所有方法均应用于收益率数据。系统还可按需从生成该结果的程序包中，复现一项关于金融传染的配套研究的主要发现。该平台是涵盖三次软件发布与三篇预印本的活跃研究计划的核心工作，目前已在公共地址免费开源运行。我们的目标简单明确：缩短从研究主张到他人能独立验证之间的时间距离。