Skill UI

This guide outlines the rigorous standards required for submitting econometrics papers to top-tier academic journals. It focuses on ensuring the integrity of identification assumptions, linking theoretical claims to empirical data, structuring proofs correctly, and anticipating referee critiques (e.g., object drift, assumption opacity). Essential for advanced academic researchers writing methods papers.

Provides a comprehensive, structured strategy for authors responding to referee reports and revising highly technical econometric manuscripts for submission to top-tier journals like The Econometrics Journal (EctJ). It guides authors on differentiating between validity-critical, applied-value-critical, and compression-risk issues, ensuring the response is rigorous, concise, and systematically addresses all methodological and empirical gaps.

A comprehensive guide for authors preparing manuscripts for submission to the International Economic Review (IER). It teaches authors how to anticipate the rigorous objections of expert referees across various archetypes (theory, econometrics, micro, empirical). Users learn the pre-mortem process to identify weak assumptions, strengthen identification arguments, and structure the cover letter to maximize the paper's chances of acceptance.

This guide details the rigorous, reproducible empirical strategies required to identify causal parameters in applied econometrics, meeting high standards (e.g., JAE). It covers various designs—including DID, IV, panel data, and time-series—and mandates that every assumption and diagnostic test must be explicitly stated, tested, and reproducible using deposited code/data.

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 authors in spatial economics on conducting rigorous robustness checks. It systematically addresses common spatial threats—such as the Modifiable Areal Unit Problem (MAUP), spatial weight matrix sensitivity, and spatial autocorrelation—to ensure published results are scientifically sound and not artifacts of the data definition or model specification. Essential for high-level academic submissions.

This guide provides a comprehensive framework for rigorously identifying and defending the methodological core of academic papers in finance and banking. It covers both empirical tracks (e.g., staggered DID, RDD, causal design for bank data) and theoretical tracks (defining necessary assumptions and analytical discipline). It helps researchers move beyond default methods to address complex challenges like internal versus external validity and causality separation.

This guide provides a detailed strategy for authors responding to reviewers' comments (R&R or reject-and-resubmit) for top-tier academic journals, particularly in finance and econometrics. It teaches systematic methods for structuring the rebuttal letter, addressing measurement artifacts, managing endogeneity concerns, and maintaining the transparency of core results to maximize acceptance chances.

A comprehensive guide for stress-testing causal identification in empirical microeconometrics. It covers advanced methods like RCT, DID, RDD, and IV, emphasizing robustness checks, identifying selection bias, and structuring results for clear public policy interpretation in high-impact academic journals.

This tool assists authors in positioning their academic manuscripts for the Journal of Macro-quality Research. It provides deep insights into the journal's scope, methodological preferences (macro econometrics, CGE, quality measurement), structural requirements, and common rejection pitfalls. It helps users tailor their writing and submission strategy to maximize acceptance chances.

A comprehensive framework for conducting and structuring robustness checks in academic papers, particularly in econometrics and behavioral economics. It guides researchers on identifying specific threats to the core parameter (e.g., functional form dependence, incentive bias, sample selection) and organizing tests by threat, ensuring the findings are statistically reliable and withstand expert scrutiny.

This advanced analytical skill focuses on rigorously identifying and testing the specific transmission channels through which external shocks affect an economy. Essential for high-impact international economics research, it moves beyond descriptive mechanism narratives by focusing on testable proxies across trade, investment, finance, and expectation channels. It requires advanced econometrics (e.g., mediation analysis, sample splitting) and careful identification strategy to ensure causal claims are robust.