Skill UI

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 expert standards for creating rigorous, self-contained tables and figures for top-tier academic journals (like JFE). It covers detailed conventions, such as structuring exhibits to convey a single, clear message, consistently reporting statistical metrics (SEs/t-stats), and ensuring figures visualize patterns rather than merely decorate. Essential for submitting polished, publication-ready empirical research.

A detailed guide for constructing a comprehensive robustness ledger in empirical financial research, particularly for microstructural data. It structures checks to address common academic threats, such as measurement bias, sample selection, microstructure noise (e.g., bid-ask bounce), and flawed inference, ensuring findings are methodologically sound and highly defensible in top-tier journals.

Provides a comprehensive framework for conducting and reporting robustness checks in health economics studies. It guides authors to organize findings by addressing specific threats to the core claim (e.g., selection bias, concurrent policy changes, measurement error) rather than simply appending multiple specifications. Essential for ensuring results are stable and defensible for top-tier journal submissions.

Provides a comprehensive guide for structuring the robustness section of international macro-finance papers, specifically tailored for JIMF submissions. It maps empirical checks to named international-finance threats (e.g., US-centrism, global episodes, regime dependence) to ensure results are stable and credible. Covers advanced econometric techniques like Driscoll–Kraay and time fixed effects.

A comprehensive guide to establishing credible causal identification in empirical law and economics (L&E). It addresses the high institutional bar required by top journals, detailing how to defend the source of variation (e.g., reforms, governance choices, court assignments) against common threats like endogenous timing, selection bias, and omitted variables. Covers advanced methods including staggered Difference-in-Differences (DiD), Instrumental Variables (IV), and Regression Discontinuity Design (RDD).

A comprehensive guide on structuring robustness checks for academic papers, particularly for Law, Economics, and Organization journals. Instead of simply listing additional tables, this method teaches how to organize checks by specific theoretical threats (selection bias, endogeneity, measurement error, alternative mechanisms) to strengthen the core institutional claim and anticipate referee criticism.

This guide provides a structured methodology for designing a comprehensive robustness suite for high-stakes academic papers, particularly in monetary economics and banking. Instead of running arbitrary checks, it maps diagnostic tests directly to named econometric threats (e.g., shock contamination, specification dependence, inference understatement, regime shifts), ensuring the credibility and rigor of empirical findings.

A comprehensive guide for designing and presenting rigorous experiments for submissions to the Journal of Software (JOS). It guides users through setting clear Research Questions (RQ), selecting real-world datasets and baselines, applying advanced statistical testing (effect size, significance), conducting ablation studies, and addressing threats to validity (internal, external, conceptual). This ensures the evidence strongly supports the claims, meeting top-tier academic standards.

A comprehensive guide to advanced econometric identification strategies required for high-stakes public finance research. Covers techniques like Regression Discontinuity Design (RDD), Difference-in-Differences (DiD), Bunching, and Instrumental Variables (IV). It stresses the need to defend policy-induced discontinuities and manage threats of endogeneity, improving the credibility of empirical claims.

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.

A comprehensive style guide for revising research papers to meet the specific, high standards of KDD conferences. It details the expected structure of the first page, emphasizing data regimes, named mechanisms, and quantifiable evidence (scale, metrics). It also provides rules for differentiating research vs. applied data science (ADS) voices and optimizing content for two-column, page-limited formats.