Skill UI

This guide outlines the rigorous requirements for preparing a reproducible package for Quantitative Economics (QE) journal submissions. It details the strict standards set by the Econometric Society Data Editor, covering raw data, code, documentation, and handling complex data exemptions to ensure the submission passes the pre-acceptance reproducibility check (DCAS compliant).

Provides detailed guidelines for constructing high-quality tables and figures for Quantitative Economics (QE) manuscripts. It emphasizes adhering to strict academic standards, such as replacing significance asterisks with standard errors and confidence bands, placing exhibits in-text, and ensuring all visuals are self-contained, professionally formatted, and journal-compliant.

This guide helps researchers determine if their academic project is best suited for Quantitative Economics (QE) among top economic journals. It provides a structured fit test, guiding users to sharpen the substantive economic question and confirm that the quantitative method is necessary—not merely decorative—to answer it. It helps situate research by comparing QE against pure theory and method-first outlets.

This guide helps authors polish the prose, abstract, and introduction of quantitative economics manuscripts for a general-interest Econometric Society readership. It emphasizes structuring the paper to clearly present both a substantive economic question and a quantitative method. It adheres to QE house rules, requiring findings to be communicated through point estimates with standard errors and confidence sets, rather than relying on statistical asterisks.

A comprehensive guide for strengthening causal identification strategies (DID, IV, RDD, RCT) for high-stakes academic publications. It details modern econometric estimators, required diagnostic checks (pre-trends, density tests, weak-IV robustness), and the philosophical shift from technique to answering a big economic question, essential for submitting to top journals like the Quarterly Journal of Economics (QJE).

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

This skill helps authors assess the suitability of their structural, computational, or econometric manuscript for top-tier quantitative economics journals. It guides authors on meeting the high bar for model rigor, identification transparency, and computational evidence required for publication in this field, acting as a comprehensive fit and re-framing tool.

A comprehensive guide detailing the stringent methodological and reporting standards required for submitting medical imaging research (diagnostic, AI, interventional) to a top-tier radiology journal. It covers expectations for diagnostic accuracy (STARD), AI model validation (CLAIM), sample size justification, reproducibility, and ensuring clinical relevance, helping authors frame their work for optimal journal fit.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

A comprehensive guide to the rigorous academic peer review process, exemplified by top-tier Recommender Systems conferences (RecSys). It details the double-blind review mechanics, key evaluation criteria (novelty, validity, reproducibility), the critical rebuttal phase, and the requirements for final publication in ACM Digital Library. Essential for researchers and students aiming to improve academic submission quality.

This comprehensive guide helps authors structure the inferential backbone of economic papers for top journals. It provides detailed checklists for theoretical models (assumptions, regularity), empirical studies (causal design, identification), and computational methods (convergence, asymptotics), ensuring claims are robust and credible across different paper types.