v1.9.0

What's Changed

This new panels module (spreg version 1.9.0) offers the following panel functions (in addition to the SUR estimators):

(see examples in https://github.com/pysal/spreg/blob/6abf15bfed2c0f9a02573ddb29d6492a305736ef/notebooks/panels_examples.ipynb)

1. OLS and Basic Panel Classes

• PooledOLS
  Description: Baseline pooled regression for panel data. Initializes and runs the pooled OLS estimation with optional BSK spatial diagnostics.

• PanelFE
  Description: Fixed Effects (Within) estimator for panel data. Performs the "Within Transformation" (demeaning) and executes the regression.

• PanelRE
  Description: Random Effects (GLS) estimator. Estimates variance components using Swamy-Arora, computes quasi-demeaning, and performs the Hausman test.

2. GMM-Based Spatial Error Classes

• GM_ErrorPooled
  Description: Pooled Spatial Error Model (SEM) via GMM. Based on the heteroskedasticity-robust estimator proposed by Arraiz2010, available in the spreg function GM_Error_Het.

• GM_ErrorRE
  Description: KKP spatial random effects model.

3. Maximum Likelihood (ML) Spatial Classes

• ML_ErrorPooled
  Description: Pooled ML SEM with diagnostic output.

• ML_ErrorFE
  Description: ML estimation for Fixed Effects Spatial Error models.

• ML_ErrorRE
  Description: ML estimation for Random Effects Spatial Error models using Ord's eigenvalue approach.

• ML_LagFE
  Description: ML estimation for Fixed Effects Spatial Lag models with spatial impacts.

• ML_LagRE
  Description: ML Spatial Lag Random Effects to estimate both the spatial lag (ρ) and the random effects component (ϕ)

Other Changes

• Minor fixes

Full Changelog: v1.8.5...v1.9.0