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# SUR Models
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Seemingly unrelated regressions with spatial error and lag structures for simultaneous equation systems with cross-equation correlation.
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## Capabilities
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### Basic SUR Model
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```python { .api }
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class SUR:
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    def __init__(self, bigy, bigX, df_name=None, sur_constant=True, name_bigy=None, 
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                 name_bigX=None, name_ds=None, vm=False, latex=False):
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        """
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        Seemingly unrelated regressions.
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        Parameters:
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        - bigy (array): Stacked dependent variables for all equations
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        - bigX (array): Stacked independent variables for all equations
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        - df_name (list): Names for each equation
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        - sur_constant (bool): Include constant terms
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        """
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```
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### Spatial SUR Models
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```python { .api }
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class SURerrorGM:
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    def __init__(self, bigy, bigX, w, df_name=None, sur_constant=True):
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        """SUR with spatial error structure using GMM estimation."""
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class SURerrorML:
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    def __init__(self, bigy, bigX, w, df_name=None, sur_constant=True):
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        """SUR with spatial error structure using ML estimation."""
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class SURlagIV:
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    def __init__(self, bigy, bigX, w, df_name=None, sur_constant=True):
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        """SUR with spatial lag structure using IV estimation."""
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```