## throwing in a spatially-correlated random effect may mess up the fixed effect you love - revisiting Hodges and Reich (2010) for SAR models

import pysal as ps import numpy as np import pandas as pd import matplotlib.pyplot as plt import geopandas as gpd %matplotlib inline This is just a quick demonstration of what I understand from Hodges & Reich (2010)’s argument about the structure of spatial error terms. Essentially, his claim is that the substantive estimates ($\hat{\beta}$) from an ordinary least squares regression over $N$ observations and $P$ covariates:
$$ Y \sim \mathcal{N}(X\hat{\beta}, \sigma^2)$$
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