Skill UI

A comprehensive guide designed to help researchers pre-empt the complex and specific objections raised by referees in law-and-economics journals like JLE. It addresses the unique requirements of single-blind review, focusing on institutional accuracy, methodological robustness, and structuring the manuscript to anticipate critical questions before submission.

A comprehensive guide and package structure for preparing empirical law and economics manuscripts for journal submission, ensuring full data and code reproducibility prior to publication. It mandates the assembly of data files, transformation scripts, a master execution script, and detailed README documentation, explicitly addressing the unique challenges of restricted or proprietary legal data sources (e.g., sealed court records).

A comprehensive guide for performing rigorous robustness checks on causal estimates in law and economics. It teaches researchers how to pre-empt common referee concerns—such as specification search, alternative treatment dating, or contaminated controls—by implementing targeted, methodologically sound tests to ensure estimates are stable and credible.

A comprehensive guide for formatting and creating high-impact tables and figures for academic submissions, particularly adhering to The Journal of Law and Economics (JLE) standards. It teaches how to visually present the main effect of a legal rule (e.g., event-studies, RD, DiD) clearly and concisely, ensuring the core identification is legible at a glance. Covers best practices for standard errors, clustering, and self-contained academic notes.

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).

This guide details how to assemble a comprehensive, reproducible replication package for academic manuscripts, especially in fields relying on complex institutional data (e.g., court records, contracts). It mandates creating a one-command master script, documenting the entire data pipeline from raw sources to final exhibits, and providing detailed protocols for hand-coded variables and data provenance. The goal is to ensure that any researcher can perfectly reproduce every finding.

This guide provides best practices for designing tables and figures for economics manuscripts, specifically following the conventions required by journals like JLEO. It emphasizes foregrounding the institutional or organizational comparison, ensuring exhibits are self-contained, and converting implied arguments (like comparative statics or event studies) into visible, persuasive graphical evidence.

A guide for formalizing complex institutional arguments into rigorous, testable economic models, specifically tailored for submission to top journals like JLEO. It covers foundational lineages—such as transaction-cost economics, property rights, and principal-agent problems—teaching researchers how to right-size models, justify assumptions institutionally, and ensure comparative statics are the primary deliverable for empirical testing.

A comprehensive guide for conducting and reporting rigorous statistical analysis for academic marketing manuscripts, particularly tailored for the Journal of Marketing (JM) standards. It covers selecting appropriate estimators for various designs (e.g., panel, choice, field experiments), managing identification, testing robustness, and adhering to the mandatory reporting of p-values, standard errors, and managerial effect sizes. Also covers data replication and advanced econometric techniques.

Provides comprehensive guidelines for building high-quality, statistically rigorous tables and figures for Journal of Marketing (JM) submissions. It emphasizes adhering to AMA house style, ensuring results display exact p-values, standard errors, and interpretable effect sizes, while strictly managing the 50-page manuscript budget.

A comprehensive guide to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.

A comprehensive guide for establishing rigorous identification strategies in monetary, banking, and macro-financial modeling. It details the standards required for publishing causal claims in top-tier journals (like JMCB), covering three branches: macro shock identification (SVAR), structural parameter identification, and micro-banking causal designs (e.g., staggered regulations). Learn how to defend against information effects, address endogeneity, and ensure policy relevance.