classmethod IVSystemGMM.from_formula(formula, data, *, weights=None, weight_type='robust', **weight_config)[source]

Specify a 3SLS using the formula interface

formula{str, dict-like}

Either a string or a dictionary of strings where each value in the dictionary represents a single equation. See Notes for a description of the accepted syntax


Frame containing named variables


Dictionary like object (e.g. a DataFrame) containing variable weights. Each entry must have the same number of observations as data. If an equation label is not a key weights, the weights will be set to unity


Name of moment condition weight function to use in the GMM estimation. Valid options are:

  • “unadjusted”, “homoskedastic” - Assume moments are homoskedastic

  • “robust”, “heteroskedastic” - Allow for heteroskedasticity


Additional keyword arguments to pass to the moment condition weight function


Model instance


Models can be specified in one of two ways. The first uses curly braces to encapsulate equations. The second uses a dictionary where each key is an equation name.


The simplest format uses standard formulas for each equation in a dictionary. Best practice is to use an Ordered Dictionary

>>> import pandas as pd
>>> import numpy as np
>>> cols = ["y1", "x1_1", "x1_2", "z1", "y2", "x2_1", "x2_2", "z2"]
>>> data = pd.DataFrame(np.random.randn(500, 8), columns=cols)
>>> from linearmodels.system import IVSystemGMM
>>> formula = {"eq1": "y1 ~ 1 + x1_1 + [x1_2 ~ z1]",
...            "eq2": "y2 ~ 1 + x2_1 + [x2_2 ~ z2]"}
>>> mod = IVSystemGMM.from_formula(formula, data)

The second format uses curly braces {} to surround distinct equations

>>> formula = "{y1 ~ 1 + x1_1 + [x1_2 ~ z1]} {y2 ~ 1 + x2_1 + [x2_2 ~ z2]}"
>>> mod = IVSystemGMM.from_formula(formula, data)

It is also possible to include equation labels when using curly braces

>>> formula = "{eq1: y1 ~ 1 + x1_1 + [x1_2 ~ z1]} {eq2: y2 ~ 1 + x2_1 + [x2_2 ~ z2]}"
>>> mod = IVSystemGMM.from_formula(formula, data)
Return type