Description

This Master Prompt focuses on transforming raw longitudinal, repeated-measures, spatial-temporal, or multidimensional observations into a transparent, internally consistent, and analysis-ready panel dataset before formal model estimation. It guides researchers through the complete data-construction chain: defining cross-sectional and temporal identifiers; establishing the observational unit; determining \(N\), \(T\), and any additional dimensions; checking unit–time uniqueness; distinguishing balanced from unbalanced structures; documenting entry, exit, attrition, and temporal gaps; harmonizing measurement frequencies; constructing dependent, explanatory, control, interaction, lagged, transformed, instrumental, factor-related, and spatial variables; and producing a reproducible final analytical sample. The need for this stage is particularly important because modern panel datasets may contain complex structures extending well beyond conventional balanced \(i\times t\) observations, including three-dimensional panels and high-dimensional covariate or instrument spaces. Three-dimensional panel models, for example, can involve multiple fixed-effect dimensions and regressors indexed simultaneously by \(i\), \(j\), and \(t\). Mundlak-estimators-for-three-di… Mundlak-estimators-for-three-di…