Module Reference

Models for Panel Data

PanelOLS(dependent, exog, *[, weights, ...])

One- and two-way fixed effects estimator for panel data

RandomEffects(dependent, exog, *[, weights, ...])

One-way Random Effects model for panel data

BetweenOLS(dependent, exog, *[, weights, ...])

Between estimator for panel data

FirstDifferenceOLS(dependent, exog, *[, ...])

First difference model for panel data

PooledOLS(dependent, exog, *[, weights, ...])

Pooled coefficient estimator for panel data

FamaMacBeth(dependent, exog, *[, weights, ...])

Pooled coefficient estimator for panel data

Estimation Results


Results container for Fama MacBeth panel data models


Results container for panel data models that do not include effects


Results container for panel data models that include effects


Results container for random effect panel data models

PanelModelComparison(results, *[, ...])

Comparison of multiple models

compare(results, *[, precision, stars])

Compare the results of multiple models

Panel Model Covariance Estimators

HomoskedasticCovariance(y, x, params, ...[, ...])

Homoskedastic covariance estimation

HeteroskedasticCovariance(y, x, params, ...)

Covariance estimation using White estimator

ClusteredCovariance(y, x, params, ...[, ...])

One-way (Rogers) or two-way clustered covariance estimation

DriscollKraay(y, x, params, entity_ids, ...)

Driscoll-Kraay heteroskedasticity-autocorrelation robust covariance estimation

ACCovariance(y, x, params, entity_ids, ...)

Autocorrelation robust covariance estimation

FamaMacBethCovariance(y, x, params, ...[, ...])

HAC estimator for Fama-MacBeth estimator

Panel Data Structures

PanelData(x[, var_name, convert_dummies, ...])

Abstraction to handle alternative formats for panel data


Convert a MI DataFrame to a 3-d structure where columns are items.

Test Data Generation

generate_panel_data([nentity, ntime, nexog, ...])

Simulate panel data for testing

PanelModelData(data, weights, other_effects, ...)

Typed namedtuple to hold simulated panel data