Skill UI

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.

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 detailing the quantitative and econometric standards required for submissions to top-tier monetary and macroeconomic journals. It covers advanced techniques like VAR/SVAR, Local Projections, and DSGE modeling, focusing on proper inference, robustness checks, and diagnostic reporting to ensure scholarly rigor.

This skill provides a rigorous framework for designing academic studies, particularly for high-impact psychological journals. It guides researchers to proactively address common methodological pitfalls such as common-method variance (CMV), unmodeled nesting, and construct validity gaps. Topics covered include temporal separation, multi-level modeling, advanced causal inference techniques (DiD, IV), and pre-registration best practices, strengthening the plan before data collection.

This guide outlines the rigorous formal requirements for econometric papers, focusing on achieving the level of mathematical rigor expected by top academic journals. It covers proving identification, verifying primitive assumptions, constructing asymptotic derivations (consistency, rate, distribution), and structuring proofs for maximum verifiability in causal inference and time series analysis.

A comprehensive guide for authors submitting financial econometrics papers. This skill helps determine if a manuscript is suitable for journals like the Journal of Empirical Finance (JEF). It guides authors on framing the core contribution—whether it lies in the empirical method, measurement, or careful evaluation—and details necessary rigor, such as out-of-sample testing, proper inference, and adherence to house style.

This guide provides an advanced framework for conducting rigorous causal inference and policy evaluation, essential for academic journals. It covers state-of-the-art methods including DiD, RDD, Synthetic Control, and experimental analysis. The core focus is moving beyond simple p-values to estimate effect magnitudes in tangible 'decision units' (e.g., dollars per household, percentage points). It mandates comprehensive stress-testing, including pre-trend checks, placebo outcomes, and detailed subgroup heterogeneity analysis, ensuring results are immediately actionable by regulators.

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.

Provides a systematic guide for authors to assess if their academic manuscript aligns with a specific journal's scope. It helps authors determine the primary contribution—whether it should be framed as quantitative measurement, forecasting, or method application, rather than relying solely on causal inference, thereby guiding optimal revisions and submission routes.

A detailed guide on establishing and reporting the necessary robustness and sensitivity analyses for manuscripts in quantitative economics and econometrics. It clarifies that the required robustness differs significantly from causal inference studies, emphasizing testing against variations in method choice, parameters, data scope, and sample periods. Provides structured methodologies for generating sensitivity matrices and discussing model stability.

A rigorous guide for advanced spatial econometrics, designed to stress-test causal claims in urban and spatial economics papers. It provides a framework to defend against the three critical spatial confounds: selection/sorting, spatial spillovers (SUTVA failure), and spatial autocorrelation. Essential for publishing high-impact work in journals like JUE.

A comprehensive guide for advanced empirical research in open economies. It outlines best practices for establishing causal inference by anchoring identification strategies in external or institutional shocks (e.g., tariffs, trade agreements, exchange rate reforms). The guide emphasizes modern critiques of standard methods like shift-share and gravity models, providing detailed protocols for DID, Bartik decomposition, and IV estimation to meet top-tier academic journal standards.