Spatial econometric regression models for analyzing geographically-related data interactions.
Overall
score
87%
Fixed effects and random effects panel data models with spatial error and lag specifications for analyzing spatially-correlated panel datasets.
class Panel_FE_Error:
def __init__(self, y, x, w, epsilon=0.0000001, hard_bound=False, vm=False,
name_y=None, name_x=None, name_w=None, name_ds=None,
latex=False):
"""
Fixed effects panel spatial error model.
Parameters:
- y (array): Dependent variable (stacked by time periods)
- x (array): Independent variables
- w (sparse matrix): Spatial weights matrix
"""
class Panel_FE_Lag:
def __init__(self, y, x, w, epsilon=0.0000001, hard_bound=False, vm=False,
name_y=None, name_x=None, name_w=None, name_ds=None,
latex=False):
"""Fixed effects panel spatial lag model."""class Panel_RE_Error:
def __init__(self, y, x, w, epsilon=0.0000001, hard_bound=False, vm=False,
name_y=None, name_x=None, name_w=None, name_ds=None,
latex=False):
"""Random effects panel spatial error model."""
class Panel_RE_Lag:
def __init__(self, y, x, w, epsilon=0.0000001, hard_bound=False, vm=False,
name_y=None, name_x=None, name_w=None, name_ds=None,
latex=False):
"""Random effects panel spatial lag model."""Install with Tessl CLI
npx tessl i tessl/pypi-spregdocs
evals
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scenario-4
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scenario-7
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