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This guide provides comprehensive standards for conducting and reporting rigorous empirical and simulation evidence in econometrics. It covers designing Monte Carlo simulations, creating minimum evidence maps, ensuring reproducibility through detailed logging, and linking theoretical claims directly to finite-sample evidence. Essential for high-level statistical research.

This guide provides strict guidelines for authors submitting econometrics papers, detailing how to strategically compress, optimize, and structure academic exhibits (tables, figures, simulations). It ensures that critical results and diagnostic information are presented clearly within tight page constraints, maximizing impact while adhering to journal standards.

This guide outlines the mandatory structure for creating a reproducible research package, required by academic journals like the European Economic Review (EER). It details how to properly assemble raw data, master analysis scripts, environmental specifications, and comprehensive documentation (README) to ensure that reported results can be independently verified by others. It is essential for any paper involving empirical analysis or simulations.

Provides rigorous guidelines for analyzing experimental game data and constructing numerical examples for high-level theoretical journals. Emphasizes that analysis must be tightly linked to theoretical claims, such as matching observed outcomes to predicted equilibria, rather than serving as general causal evidence. Covers session-level clustering, structural fitting, and ensuring computational reproducibility.

Provides comprehensive guidelines for packaging and documenting complex robotics research artifacts (code, simulations, hardware designs, datasets, etc.) for scholarly review. It outlines best practices for ensuring reproducibility, adopting the "five-minute-skeptic standard," and managing the full lifecycle of artifacts from review submission to public acceptance.

A comprehensive guide for designing, auditing, and reporting reliable robotic experiments, particularly for major conferences like IROS. It moves beyond simple success rates, detailing how to map every claim to verifiable evidence (trials, simulations, field data). Key areas covered include defining pre-set success criteria, reporting failure taxonomies, ensuring fair baselines, and quantifying the critical sim-to-real gap using advanced small-n statistical methods.

A comprehensive guide on achieving scientific reproducibility for complex computer architecture and system simulations. It details how to manage and pin all dependencies—including simulator commit hashes, local patches, workload binaries, hardware states, and post-processing scripts—to ensure that every reported figure and number can be accurately regenerated, regardless of time or environment changes.

A comprehensive guide for advanced quantitative research, focusing on demonstrating the validity of econometric methods. It outlines best practices for combining rigorous Monte Carlo simulations (stress-testing DGPs, baselines) with substantive real-world data applications, essential for publication in high-impact economics and statistics journals.

A comprehensive guide designed to stress-test the causal identification of behavioral mechanisms for top-tier economic journals (e.g., JEBO). It provides stringent criteria across lab/field experiments, observational data (DiD, IV, RDD), and simulations, ensuring the design isolates the claimed psychological or institutional channel, not just a correlation. Use this when standard econometric methods are insufficient to prove mechanism causality.

A comprehensive guide to structuring robustness checks for behavioral economics and empirical research. Instead of merely listing statistical tests, this framework mandates organizing checks by the underlying behavioral threat (e.g., experimenter demand, multiple comparisons, specification choice). It provides specific guidelines for experiments, observational data, and simulations, ensuring that findings are robust against alternative behavioral explanations and methodological choices.

This guide provides strict guidelines for authors submitting highly theoretical papers (e.g., to Journal of Economic Theory) regarding computational, numerical, or empirical evidence. It enforces the 'theory-first' principle, ensuring that all quantitative work—including simulations, examples, and counterexamples—must serve to illustrate, test, or deepen the main theoretical contribution, rather than standing as a standalone finding. Key requirements include maintaining reproducibility, keeping examples minimal, and proper structural placement.

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.