linearmodels.panel.model.FirstDifferenceOLS.fit¶
-
FirstDifferenceOLS.fit(*, cov_type: str =
'unadjusted'
, debiased: bool =True
, **cov_config: bool | float | str | ndarray[Any, dtype[int64]] | DataFrame | PanelData) PanelResults [source]¶ Estimate model parameters
- Parameters:¶
- cov_type: str =
'unadjusted'
¶ Name of covariance estimator. See Notes.
- debiased: bool =
True
¶ Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment.
- **cov_config: bool | float | str | ndarray[Any, dtype[int64]] | DataFrame | PanelData¶
Additional covariance-specific options. See Notes.
- cov_type: str =
- Returns:¶
Estimation results
- Return type:¶
Examples
>>> from linearmodels import FirstDifferenceOLS >>> mod = FirstDifferenceOLS(y, x) >>> robust = mod.fit(cov_type="robust") >>> clustered = mod.fit(cov_type="clustered", cluster_entity=True)
Notes
Three covariance estimators are supported:
“unadjusted”, “homoskedastic” - Assume residual are homoskedastic
“robust”, “heteroskedastic” - Control for heteroskedasticity using White’s estimator
“clustered` - White’s. Configuration options are:
clusters
- Input containing 1 or 2 variables. Clusters should be integer values, although other types will be coerced to integer values by treating as categorical variablescluster_entity
- Boolean flag indicating to use entity clusters
“kernel” - Driscoll-Kraay HAC estimator. Configurations options are:
kernel
- One of the supported kernels (bartlett, parzen, qs). Default is Bartlett’s kernel, which is produces a covariance estimator similar to the Newey-West covariance estimator.bandwidth
- Bandwidth to use when computing the kernel. If not provided, a naive default is used.
When using a clustered covariance estimator, all cluster ids must be identical within a first difference. In most scenarios, this requires ids to be identical within an entity.