PanelOLS

PanelOLS is a class from the Python `linearmodels` library that implements the ordinary least‑squares estimator for panel (longitudinal) data. It extends the standard OLS routine to accommodate fixed‑effects, random‑effects, and other advanced specifications specific to panel structures. The model is specified by a design matrix containing both individual or cross‑section identifiers and time identifiers, allowing the statistician to control for unobserved heterogeneity across units and over time.

Key features of PanelOLS include automatic handling of cluster‑robust standard errors, support for multiple groups and nested identifiers, and the ability to drop groups with insufficient within‑group variation. The estimator can also be augmented with instrumental‑variable or generalized method of moments procedures via the `PanelIV` subclass. Users can integrate the estimator into SciPy and Pandas workflows, benefiting from familiar data‑handling interfaces and efficient sparse matrix operations.

Typical applications of PanelOLS appear in economics, finance, and social sciences where repeated observations on entities—such as firms, households, or countries—are available. It is frequently employed to estimate policy impacts, productivity dynamics, or market regressions while controlling for entity‑specific effects that would otherwise bias ordinary least‑squares estimates.

Installation is straightforward through pip (`pip install linearmodels`). The library’s documentation provides detailed examples and guides on model specification, inference, and diagnostic testing. PanelOLS is open source and distributed under the BSD license, ensuring accessibility and the ability for researchers to contribute enhancements.