Skill UI

This guide outlines the mandatory data and code deposit policy for articles published in the Journal of Applied Econometrics (JAE). Authors must submit a complete, non-confidential set of input data (plain ASCII/CSV), programs that replicate every result, and a detailed README file. It emphasizes strict adherence to archival format rules, ensuring the research is fully reproducible for the academic community.

Provides a comprehensive guide to the Journal of Applied Econometrics (JAE) submission and review process. It details the single-blind peer review structure, the final decision authority of the Editor-in-Chief, and the high standards for empirical rigor, including data reproducibility, appropriate inference methods, and mandatory data archiving. Essential for authors planning submissions and resubmissions.

This guide provides best practices for structuring tables and figures in academic econometrics manuscripts, especially when adhering to strict journal page limits. It advises researchers on exhibit triage—determining which core findings belong in the main text versus supplementary online appendices—and guarantees reproducibility by detailing required note components and generating scripts.

This resource guides researchers through the rigorous criteria for publishing in the Journal of Applied Econometrics (JAE). It helps determine if a paper is sufficiently 'applied' (focusing on real data and economic questions) rather than purely theoretical. It outlines the necessity of replicability, the mandatory data archiving process, and the strategic choice between a standard Research Article and a Replication Article.

A comprehensive orchestrator skill guiding the entire lifecycle of an applied econometrics manuscript targeting the Journal of Applied Econometrics (JAE). It ensures the paper maintains an applied, real-data focus and rigorously follows the necessary steps, including literature positioning, identification strategy, reproducible data analysis, and mandatory data archival deposit. Designed to prevent common pitfalls by integrating data integrity checks at every stage.

A comprehensive guide for executing and reporting the full empirical robustness battery required by top accounting journals. It covers modern econometric techniques like standard error clustering, advanced Difference-in-Differences (DiD) estimators for staggered timing, Regression Discontinuity (RD) diagnostics, and Instrumental Variables (IV/2SLS). The guide emphasizes rigorous validation of accounting constructs, executing multiple sensitivity tests, and ensuring full data-and-code reproducibility for academic publication.

A comprehensive guide for designing robust, causal econometric studies using large-sample archival data. It details how to identify valid sources of variation (e.g., DiD, RD, IV), mitigate endogeneity threats (e.g., reverse causality, selection bias), and construct reproducible data pipelines required for top-tier academic research in accounting.

A comprehensive guide for authors handling a journal's revise-and-resubmit (R&R) decision. It outlines the critical sequence: prioritizing new analyses (identification, channel, measurement) over writing, triaging referee concerns (first-order vs. second-order), drafting evidence-backed responses, and updating the complete data-and-code package. Essential for quantitative researchers aiming for publication.

A comprehensive guide for finalizing the core evidence—tables and figures—for high-stakes empirical accounting or economics papers (e.g., Journal of Accounting Research). It outlines the mandatory 'exhibit set,' covering sample construction, descriptive statistics, correlation matrices, main regression results, and crucial identification plots (DiD, RD). Focuses on adhering to rigorous house styles and ensuring all claims are data-supported.

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.

This comprehensive guide details the rigorous methodological standards required for validating new statistical estimators, tests, or algorithms. It moves beyond simple causal inference, emphasizing foundational proofs of identification, regularity conditions, asymptotic consistency, and finite-sample robustness. It serves as a checklist for assuring the credibility and reliability of methods in academic statistical journals.

This guide assists researchers in assembling a comprehensive, reproducible data and code supplement for submissions to statistical journals, adhering to the American Statistical Association (ASA) data-sharing policies. It outlines the required structure (README, code, data), provenance tracking, environment pinning, and necessary steps (like setting random seeds and drafting data availability statements) to ensure that published results can be fully regenerated and verified by reviewers.