Skill UI

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.

This guide outlines the creation of a complete, verifiable research package for economic policy manuscripts. It ensures that all empirical, experimental, or simulation results are fully reproducible by providing raw data guides, cleaning scripts, analysis code, and a single 'master' script that runs end-to-end. This process is critical for academic acceptance, allowing reviewers and discussants to independently verify every figure, table, and key number reported in the paper.

Provides a comprehensive guide on generating and reporting rigorous Monte Carlo and finite-sample evidence for econometrics and statistical methods papers. It details best practices for designing simulations, stress-testing assumption boundaries (degenerate cases), comparing against existing estimators, and ensuring full reproducibility to validate theoretical results for realistic sample sizes.

This guide details the principles of conducting rigorous Monte Carlo simulations for econometric theory papers. It instructs users on designing simulations that test finite-sample behavior, ensuring that asymptotic approximations are useful and accurate across boundary cases (e.g., weak identification, heavy tails). Emphasis is placed on reproducibility, comprehensive metrics (bias, coverage, power), and ensuring the simulation supports the core theory.

This guide outlines best practices for submitting theoretical econometrics manuscripts to ET. It addresses how to handle extensive proofs, auxiliary lemmas, and simulation results that exceed page limits. The core focus is utilizing the voluntary Supplementary Material policy to ensure full reproducibility and transparency without requiring mandatory data or code archives.

A comprehensive guide detailing the rigorous standards for presenting statistical exhibits in high-level econometric theory journals. It covers best practices for Monte Carlo simulations, constructing diagnostic plots (size, power, coverage, bias), ensuring legibility at print reduction, and adhering to specific file formats (TIFF/EPS/PDF).

This router guides the entire lifecycle for writing complex, theory-driven academic manuscripts, particularly in fields like econometrics. It provides a structured roadmap spanning from initial topic selection and literature positioning through rigorous proof development, data simulation, drafting style, and the final submission/rebuttal phases. Use this resource when you are unsure of the correct sequential step or need a systematic check of all necessary components before finalizing your work.

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.

This central routing skill guides authors through the complex process of journal selection for engineering and technology manuscripts. It does not replace a single journal submission but first classifies the content based on the engineering sub-discipline, contribution type (e.g., method, system, device), validation shape (e.g., simulation, field experiment), and intended audience breadth. It then routes the paper to the most specialized and appropriate journal skill for accurate re-framing and submission.