Skill UI

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.

A comprehensive planning tool for developmental psychology researchers designing studies that must support a claim of change. It systematically guides users through critical methodological issues, including addressing age vs. cohort confounds, planning for measurement invariance across ages, handling attrition, and properly justifying sample size for growth slopes. Essential for strengthening the pre-analysis plan of longitudinal and cross-sectional designs.

A comprehensive skill guide designed to help researchers assess the suitability of their economics manuscripts for submission to 《Economic Review》. It covers journal positioning, required empirical rigor, structural expectations (abstract, citation style), common rejection pitfalls, and advice on necessary revisions and alternative journals. This tool is crucial for navigating academic publishing standards in Chinese economics.

A comprehensive guide detailing advanced econometric methods for establishing causal relationships in policy evaluations. It covers techniques such as Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and structural modeling. The core focus is bridging the gap between econometric rigor and policy legibility, ensuring that the causal mechanism is explicit in the main text while maintaining formal defense in the appendix.

This guide outlines rigorous standards for incorporating theoretical and quantitative models into economic policy manuscripts. It emphasizes that models must be minimal, transparent, and directly bear policy weight—converting estimates into statements about welfare, fiscal costs, or clear mechanisms. It teaches how to design models that pass 'the necessity test,' ensuring policymakers can trace headline numbers directly back to key, identifiable assumptions.

A comprehensive pre-submission strategy guide designed to 'red-team' theoretical and econometric manuscripts. This tool helps authors anticipate and pre-empt harsh critiques from expert co-editors and referees, focusing specifically on identifying proof gaps, testing the generality of claims, ensuring finite-sample evidence, and addressing potential methodological competitors before journal submission.

Provides a comprehensive guide on generating and reporting rigorous Monte Carlo and finite-sample evidence for econometrics and statistical methods papers. It details best practices for designing simulations, stress-testing assumption boundaries (degenerate cases), comparing against existing estimators, and ensuring full reproducibility to validate theoretical results for realistic sample sizes.

This guide details the principles of conducting rigorous Monte Carlo simulations for econometric theory papers. It instructs users on designing simulations that test finite-sample behavior, ensuring that asymptotic approximations are useful and accurate across boundary cases (e.g., weak identification, heavy tails). Emphasis is placed on reproducibility, comprehensive metrics (bias, coverage, power), and ensuring the simulation supports the core theory.

This guide provides comprehensive standards for conducting and reporting rigorous empirical and simulation evidence in econometrics. It covers designing Monte Carlo simulations, creating minimum evidence maps, ensuring reproducibility through detailed logging, and linking theoretical claims directly to finite-sample evidence. Essential for high-level statistical research.

This skill guides users through strengthening the causal identification strategy of empirical economic manuscripts (DiD, IV, RDD, Experiments). It helps audit research designs against general academic credibility standards, ensuring that assumptions, potential biases, and the link between data variation and causal objects are explicitly stated and robustly defended for diverse expert audiences.

A comprehensive guide for authors preparing manuscripts for top economics journals (e.g., EER). It details best practices for designing self-contained tables and figures, emphasizing the correct reporting of standard errors (SEs), confidence intervals (CIs), and interpreting results cleanly. This guide ensures exhibits adhere to rigorous academic publishing standards, moving beyond simple star counts to let the magnitude of the result speak for itself.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.