linearmodels.system.model.IV3SLS.from_formula¶
- classmethod IV3SLS.from_formula(formula, data, *, sigma=None, weights=None)[source]¶
Specify a 3SLS using the formula interface
- Parameters:
- 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
- data
DataFrame
Frame containing named variables
- sigmaarray_like
Prespecified residual covariance to use in GLS estimation. If not provided, FGLS is implemented based on an estimate of sigma.
- weightsdict-like
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
- formula{
- Returns:
- model
IV3SLS
Model instance
- model
Notes
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.
Examples
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 IV3SLS >>> formula = {"eq1": "y1 ~ 1 + x1_1 + [x1_2 ~ z1]", ... "eq2": "y2 ~ 1 + x2_1 + [x2_2 ~ z2]"} >>> mod = IV3SLS.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 = IV3SLS.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 = IV3SLS.from_formula(formula, data)