Empirical Methodology Writing Guide | Model Specification & Endogeneity

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AcademicIdeas offers step-by-step guidelines for structuring empirical research methodology, covering model specifications, variable definitions, and endogeneity checks.

• Standardize econometric model specifications and map independent/dependent variables
• Define rigorous sample selection rules (e.g. excluding ST, financial stocks, or missing values)
• Structure endogeneity and robustness test descriptions (e.g. lagged variables, alternative proxies)
• Equation formatting: Render formulas using standard LaTeX format or clear math layouts rather than blurry screenshots.

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• Standardize econometric model specifications and map independent/dependent variables
• Define rigorous sample selection rules (e.g. excluding ST, financial stocks, or missing values)
• Structure endogeneity and robustness test descriptions (e.g. lagged variables, alternative proxies)

Specification of Econometric Models and Equations

In empirical research, setting up a logical and scientific econometric model is the cornerstone of the methodology chapter. Equations are not just carriers of data; they are mathematical translations of your core theoretical hypotheses.

For two-way fixed effects models, clearly define the dependent variable, core independent variable, control vector, year/individual fixed effects, and the random error term to avoid review rejections.

• Equation formatting: Render formulas using standard LaTeX format or clear math layouts rather than blurry screenshots.
• Variable mappings: Every parameter and Greek symbol in your equation must be explicitly defined below the formula.
• Dimensional index: In panel structures, specify the i and t subscripts to clearly denote individual and time indicators.

Sample Cleaning and Filtering Rules

The credibility of your regression results depends heavily on the transparency of the sample cleaning process. Obscuring your data selection steps may lead reviewers to suspect selection bias. Detail each filter from raw database to final regression sample.

For corporate datasets, list the standard filtering steps clearly in your text (e.g., excluding financial firms, omitting special treatment or ST entities, and deleting missing value rows).

• Provide justifications: Explain why certain stocks are excluded (e.g., financial institutions have different asset-liability structures).
• Sample lifecycle: Document the number of observations removed at each step to ensure transparency.
• Winsorizing details: Explicitly state whether variables were winsorized (e.g., at 1% or 5% levels) to control for extreme outliers.

Handling Endogeneity and Robustness Checks

Endogeneity (omitted variables, reverse causality, measurement errors) is the most common reason empirical papers are rejected. The methodology section must outline your strategies to address these biases early on.

Standard approaches, such as instrumental variables (2SLS), system GMM, or difference-in-differences (DID), must be paired with rigorous validation of identification assumptions.

• Instrumental variables: If using an IV, justify why it satisfies both relevance and exclusion restriction conditions.
• Robustness testing: Detail alternative specifications such as replacing key variables, altering sample periods, or utilizing lagged estimators.
• Mechanism analysis: When checking mediating or moderating effects, present path diagrams and their respective equations clearly.

Frequently asked questions

Should I include my raw Stata or R code in the methodology section?

No, the methodology section focuses on econometric models and statistical logic. Code should be omitted or submitted in an appendix.

Should I include as many control variables as possible?

No. Excessive control variables lead to multicollinearity and reduce degrees of freedom. Choose controls based on established literature.

How do I justify the safety of multicollinearity in my model?

State that you ran a Variance Inflation Factor (VIF) test. An average VIF value below 10 indicates that multicollinearity is not a threat.