Skill UI

A comprehensive guide for designing rigorous Monte Carlo simulations and empirical illustrations for methodological papers, particularly in econometrics. It details how to stress assumptions, report necessary metrics (size, power, bias), and structure the evidence to prove a method's theoretical validity and practical utility.

This guide outlines the rigorous formal requirements for econometric papers, focusing on achieving the level of mathematical rigor expected by top academic journals. It covers proving identification, verifying primitive assumptions, constructing asymptotic derivations (consistency, rate, distribution), and structuring proofs for maximum verifiability in causal inference and time series analysis.

A comprehensive guide for authors responding to highly technical referee reports, particularly for submissions to top econometrics journals like the Journal of Econometrics (JoE). It details strategies for addressing objections concerning assumptions, asymptotic gaps, and Monte Carlo simulations, ensuring the revised manuscript and response letter are technically airtight and methodologically rigorous.

Provides comprehensive guidelines for authors submitting to the Journal of Econometrics (JoE). It details the required best practices for data citation (using the Elsevier `[dataset]` format), ensuring methodological reproducibility (e.g., Monte Carlo simulations), and preparing estimators as runnable code packages (R, Python, etc.). This guide is essential for meeting rigorous academic publication standards and passing peer review.

This guide provides a comprehensive, step-by-step walkthrough of the Journal of Econometrics (JoE) submission and review lifecycle. It covers the single-anonymized review structure, the crucial initial editor screening process, and details the three submission tracks (Regular, Annals, Themed). It also outlines the rigorous methodological criteria expected from modern econometric submissions, including the required elements for the initial editorial packet.

This comprehensive guide provides authors with a final preflight checklist for submitting manuscripts to high-stakes academic journals, such as the Journal of Econometrics (JoE). It meticulously covers technical formatting rules (e.g., source files, spacing, abstract length), journal-specific processes (e.g., single-anonymized review, fee payment), and navigating complex submission portals (Editorial Manager vs. Editorial Express). Use this tool as a final quality check before hitting 'submit' to ensure compliance and maximize acceptance chances.

A comprehensive guide detailing the rigorous standards for generating tables and figures intended for top-tier econometrics journals, such as the Journal of Econometrics (JoE). It covers best practices for Monte Carlo simulations, separating size and power reporting, comparing multiple estimators, and ensuring all exhibits are statistically rigorous, reproducible, and legible in a single-PDF print format.

This tool guides researchers in assessing the methodological fit of their empirical findings for top-tier journals like the Journal of Econometrics (JoE). It pressures-tests the proposed work against rigorous academic standards, checking for mathematical novelty, clear economic motivation, and methodological advancement. Use it when unsure of the journal scope or the true contribution type.

A comprehensive guide for conducting reproducible empirical analysis in labor economics, adhering to high academic standards (e.g., JOLE norms). It covers essential steps including sample construction from CPS/ACS/registers, advanced wage decompositions (Oaxaca/RIF), proper handling of standard errors, and robust sensitivity checks to ensure results are fully replicable.

This guide provides comprehensive standards for establishing credible causal claims in labor economics research, specifically tailored to the rigor of journals like JOLE. It covers advanced econometric techniques (DID, RDD, IV, RCT, etc.) and outlines necessary robustness checks, bias corrections, and methodological defenses to ensure research findings are statistically sound and replicable.

This guide provides a rigorous structural and strategic framework for responding to a 'Revise and Resubmit' (R&R) decision, specifically tailored for the Journal of Labor Economics (JOLE). It emphasizes drafting concise, point-by-point rebuttals, addressing complex identification challenges with robust diagnostics, reconciling conflicting referee demands, and strictly adhering to the journal's word economy and single-blind submission rules. It is a critical tool for refining advanced academic manuscripts.

This comprehensive guide outlines the mandatory standards for data and code submission for academic papers published in the Journal of Labor Economics (JOLE). It instructs researchers on how to build a complete, reproducible package, detailing the requirements for documenting the full data chain (raw to final), master scripts, environment pinning, and handling proprietary data for journal review.