linearmodels.system.model.IVSystemGMM¶
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class linearmodels.system.model.IVSystemGMM(equations: Mapping[str, Mapping[str, ndarray | DataArray | DataFrame | Series | None] | Sequence[ndarray | DataArray | DataFrame | Series | None]], *, sigma: ndarray | DataArray | DataFrame | Series | None =
None, weight_type: str ='robust', **weight_config: bool | str | float)[source]¶ System Generalized Method of Moments (GMM) estimation of linear IV models
- Parameters:¶
- equations: Mapping[str, Mapping[str, ndarray | DataArray | DataFrame | Series | None] | Sequence[ndarray | DataArray | DataFrame | Series | None]]¶
Dictionary-like structure containing dependent, exogenous, endogenous and instrumental variables. Each key is an equations label and must be a string. Each value must be either a tuple of the form (dependent, exog, endog, instrument[, weights]) or a dictionary with keys “dependent”, “exog”. The dictionary may contain optional keys for “endog”, “instruments”, and “weights”. Endogenous and/or Instrument can be empty if all variables in an equation are exogenous.
- sigma: ndarray | DataArray | DataFrame | Series | None =
None¶ Prespecified residual covariance to use in GLS estimation. If not provided, FGLS is implemented based on an estimate of sigma. Only used if weight_type is “unadjusted”
- weight_type: str =
'robust'¶ Name of moment condition weight function to use in the GMM estimation
- **weight_config: bool | str | float¶
Additional keyword arguments to pass to the moment condition weight function
Notes
Estimates a linear model using GMM. Each equation is of the form
\[y_{i,k} = x_{i,k}\beta_i + \epsilon_{i,k}\]where k denotes the equation and i denoted the observation index. By stacking vertically arrays of dependent and placing the exogenous variables into a block diagonal array, the entire system can be compactly expressed as
\[Y = X\beta + \epsilon\]where
\[\begin{split}Y = \left[\begin{array}{x}Y_1 \\ Y_2 \\ \vdots \\ Y_K\end{array}\right]\end{split}\]and
\[\begin{split}X = \left[\begin{array}{cccc} X_1 & 0 & \ldots & 0 \\ 0 & X_2 & \dots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \dots & X_K \end{array}\right]\end{split}\]The system GMM estimator uses the moment condition
\[z_{ij}(y_{ij} - x_{ij}\beta_j) = 0\]where j indexes the equation. The estimator for the coefficients is given by
\[\begin{split}\hat{\beta}_{GMM} & = (X'ZW^{-1}Z'X)^{-1}X'ZW^{-1}Z'Y \\\end{split}\]where \(W\) is a positive definite weighting matrix.
Methods
add_constraints(r[, q])Add parameter constraints to a model.
fit(*[, iter_limit, tol, initial_weight, ...])Estimate model parameters
from_formula(formula, data, *[, weights, ...])Specify a 3SLS using the formula interface
predict(params, *[, equations, data, eval_env])Predict values for additional data
Remove all model constraints
Properties
Model constraints
Set or get the formula used to construct the model
Vector indicating which equations contain constants
Model parameter names