linearmodels.iv.model.IVGMMCUE¶
- class IVGMMCUE(dependent, exog, endog, instruments, *, weights=None, weight_type='robust', **weight_config)[source]¶
Estimation of IV models using continuously updating GMM
- Parameters
- dependentarray_like
Endogenous variables (nobs by 1)
- exogarray_like
Exogenous regressors (nobs by nexog)
- endogarray_like
Endogenous regressors (nobs by nendog)
- instrumentsarray_like
Instrumental variables (nobs by ninstr)
- weightsarray_like,
default
None
Observation weights used in estimation
- weight_type
str
,default
“robust” Name of moment condition weight function to use in the GMM estimation
- **weight_config
Additional keyword arguments to pass to the moment condition weight function
Notes
Available weight functions are:
‘unadjusted’, ‘homoskedastic’ - Assumes moment conditions are homoskedastic
‘robust’, ‘heteroskedastic’ - Allows for heteroskedasticity by not autocorrelation
‘kernel’ - Allows for heteroskedasticity and autocorrelation
‘cluster’ - Allows for one-way cluster dependence
In most circumstances, the
center
weight option should beTrue
to avoid starting value dependence.\[\begin{split}\hat{\beta}_{cue} & =\min_{\beta}\bar{g}(\beta)'W(\beta)^{-1}g(\beta)\\ g(\beta) & =n^{-1}\sum_{i=1}^{n}z_{i}(y_{i}-x_{i}\beta)\end{split}\]where \(W(\beta)\) is a weight matrix that depends on \(\beta\) through \(\epsilon_i = y_i - x_i\beta\).
- Attributes
formula
Formula used to create the model
has_constant
Flag indicating the model includes a constant or equivalent
isnull
Locations of observations with missing values
notnull
Locations of observations included in estimation
Methods
estimate_parameters
(starting, x, y, z[, ...])- Parameters
fit
(*[, starting, display, cov_type, ...])Estimate model parameters
from_formula
(formula, data, *[, weights, ...])- Parameters
j
(params, x, y, z)Optimization target
predict
(params, *[, exog, endog, data, eval_env])Predict values for additional data
resids
(params)Compute model residuals
wresids
(params)Compute weighted model residuals
Properties
Formula used to create the model
Flag indicating the model includes a constant or equivalent
Locations of observations with missing values
Locations of observations included in estimation