Skill UI

This guide helps structure, audit, and refine the exhibits (tables and figures) for academic manuscripts, particularly in Human Relations. It ensures that the inference path from raw data to theoretical claims is visible. It covers best practices for both qualitative exhibits (data-structure models, evidence tables) and quantitative exhibits (regression models, interaction plots), while adhering to submission constraints like anonymization and word limits.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.

This comprehensive guide provides rigorous econometric strategies for establishing parameter identification, crucial for structural modeling and causal inference. It details how to prove that model parameters are uniquely determined by the observed data, moving beyond mere estimation convergence or external calibration. Essential for advanced empirical research and addressing stringent peer review standards.

This guide provides comprehensive methodological advice for academic submissions in Information Systems (IS), particularly for journals like JAIS. It covers selecting and rigorously defending diverse research designs—including behavioral experiments, causal inference (Economics of IS), design science, qualitative studies, and literature reviews. Use it when you need to validate the suitability of your methodology against your research claims and underlying phenomena.

A comprehensive guide for econometricians submitting manuscripts to top journals. It details how to structure analyses for full reproducibility, including using master scripts, pinning software versions, and exporting results to plain-text formats. Coverage includes advanced robust inference techniques such as HAC, cluster bootstrapping, and weak-IV diagnostics, ensuring the work satisfies rigorous archival standards.

A comprehensive guide detailing the best practices for drafting a rebuttal letter in response to single-blind peer reviews for applied econometrics journals. Covers critical areas like defending identification, improving inference, and ensuring robustness, while maintaining mandatory synchronization between the revised manuscript and the official data archive.

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.

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.

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.

A comprehensive methodological guide detailing the robustness checks expected by environmental economics journals (e.g., JEEM). It provides a checklist of targeted statistical tests—including spatial inference, handling omitted variables, and varying market definitions—to ensure key welfare estimates are stable and not artifacts of specification choices. Essential for academic research in environmental and resource economics.

A comprehensive guide for handling endogeneity, selection bias, and causal inference in empirical finance research, particularly targeting the high standards of journals like JFE. It details advanced econometric methodologies including Difference-in-Differences (DID), Regression Discontinuity Design (RDD), and Instrumental Variables (IV), ensuring your causal claims are rigorously defensible against referee scrutiny.

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.