arXiv 4 Jun 2026 · Econometrics · 1 citations (OpenAlex)
arXiv:2606.05705 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bhandari, Avishek and Parida, Ipsita and Sahu, Hitesh Kumar (2026) What Drives Contagion? Identifying and Attributing Cross-Border Transmission Mechanisms self | 0.644 | 2 | 2 | 100% |
| 2 | Bhandari, Avishek (2025) Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets self | 0.644 | 2 | 2 | 100% |
| 3 | Bhandari, Avishek and Parida, Ipsita (2026) Scale-Ordered Contagion: A Spectral Theory of Heterogeneous Information Adaptation in Financial Networks self | 0.644 | 2 | 2 | 100% |
| 4 | Chang, Andrew C. and Li, Phillip (2022) Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say “Often Not” | 0.644 | 2 | 2 | 100% |
| 5 | DeLong, Elizabeth R. and DeLong, David M. and Clarke-Pearson, Daniel L (1988) Comparing the Areas Under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach | 0.644 | 2 | 2 | 100% |
| 6 | Bhandari, Avishek (2026) compute-engine: Sandboxed Open-Source Econometrics Workbench (R) with an AI Analyst Chatbot self | 0.644 | 2 | 2 | 100% |
| 7 | Bhandari, Avishek (2026) econstellar: Research Workbench and Public Portal self | 0.644 | 2 | 2 | 100% |
| 8 | Kraskov, Alexander and Stögbauer, Harald and Grassberger, Peter (2004) Estimating Mutual Information | 0.644 | 2 | 2 | 100% |
| 9 | Bhandari, Avishek (2026) NEURICX: Network-Enhanced Universal Real-time Intelligence for Complex eXchange self | 0.644 | 2 | 2 | 100% |
| 10 | Sandve, Geir Kjetil and Nekrutenko, Anton and Taylor, James and Hovi… (2013) Ten Simple Rules for Reproducible Computational Research | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 23 scored citations.