Skill UI

A comprehensive guide for analyzing and reporting findings in entrepreneurial research, specifically tailored for high-level academic journals. It covers advanced econometric techniques necessary to address common data challenges in venture panel data, such as survivorship bias, selection into founding, endogeneity, and attrition. It ensures methodological rigor and robust reporting.

A comprehensive guide for conducting advanced empirical analysis required for top-tier finance journals. It covers assembling multi-source firm panels (Compustat, CRSP, etc.), constructing complex corporate finance variables, implementing advanced econometric techniques (high-dimensional fixed effects, clustered errors), and systematically planning robustness checks and replication strategies.

This comprehensive guide outlines the rigorous methodology required for empirical and theoretical growth studies, particularly for high-impact journals like JEG. It covers best practices for cross-country panel data analysis, calibration techniques, spatial inference (Conley SEs), and structuring robust manuscripts. Use this checklist when estimating growth outcomes, simulating transition paths, or conducting deep sensitivity analyses for development economics.

A comprehensive guide for planning and stress-testing empirical analysis in financial intermediation, specifically for high-impact journals. It covers the construction of bank/loan-level panel datasets, defining variables (growth vs. levels), implementing advanced econometric specifications (e.g., fixed effects), and executing substantial robustness checks (placebo periods, alternative clustering). It also guides the planning of reproducible numerical illustrations for theoretical models.

This comprehensive guide provides rigorous methodological guidelines for executing and reporting empirical analysis on cross-national, multi-level, or panel data, specifically tailored for top-tier international business journals. It covers essential checks like measurement invariance (configural to scalar), Common Method Variance (CMV), dynamic endogeneity, and appropriate estimation techniques (e.g., Multilevel SEM, GMM), ensuring research meets the highest academic standards.

This guide provides comprehensive methodological frameworks for conducting rigorous empirical analyses in international trade and macroeconomics, particularly tailored to the standards of top-tier journals like the JIE. It covers assembling bilateral trade and cross-country panels, estimating gravity models using PPML, implementing local projections, and calibrating structural open-economy models. It ensures analyses are robust, replicable, and address common referee criticisms.

This guide provides a comprehensive framework for conducting rigorous cross-national data analysis, essential for academic research in international business. It covers critical advanced statistical methods like Measurement Invariance Testing (MGCFA), Multilevel Modeling (HLM), and Multi-Group SEM. It instructs users on handling cross-level moderation, interpreting country-level effects, and ensuring results are statistically auditable across complex data structures (e.g., panels, dyads).

A comprehensive guide for constructing and validating cross-country and time-series panel data in international macro-finance. It addresses common pitfalls in measurement choices (e.g., gross vs. net flows, USD vs. NEER), sample period justification (e.g., GFC, ZLB), and frequency mismatch, ensuring methodological rigor required by top-tier journals.

A comprehensive guide for establishing rigorous causal claims in international macro-finance. This skill addresses complex econometric challenges such as global confounders, policy endogeneity, and high-frequency surprises in open-economy models. It provides methodologies for cross-country panel data, capital control analysis, and advanced inference, ensuring results meet the high standards of top-tier international finance journals.

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.

A comprehensive guide for structuring marketing research designs, particularly for journals like the Journal of Marketing (JM). It teaches researchers how to select the most appropriate method (e.g., field experiment, survey, panel data, qualitative study) that directly addresses a substantive, managerially relevant claim. The focus is on achieving both internal validity (establishing causality) and external validity (realism in the market), ensuring the method serves the core business insight rather than being an end in itself.

This comprehensive guide assists researchers in structuring and refining empirical studies for submission to top management journals. It addresses critical methodological pitfalls, including common-method bias, endogeneity, and multilevel structures. Learn how to correctly match theoretical claims (e.g., causal effects, temporal processes) to appropriate designs (experiments, panel data, meta-analysis) to ensure rigorous scientific validity and statistical integrity.