linearmodels.panel.model.RandomEffects.fit¶
- RandomEffects.fit(*, small_sample=False, cov_type='unadjusted', debiased=True, **cov_config)[source]¶
Estimate model parameters
- Parameters:
- small_samplebool
Apply a small-sample correction to the estimate of the variance of the random effect.
- cov_type
str
Name of covariance estimator (see notes). Default is “unadjusted”.
- debiasedbool
Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment.
- **cov_config
Additional covariance-specific options. See Notes.
- Returns:
RandomEffectsResults
Estimation results
Notes
Four covariance estimators are supported:
“unadjusted”, “homoskedastic” - Assume residual are homoskedastic
“robust”, “heteroskedastic” - Control for heteroskedasticity using White’s estimator
“clustered` - One- or two-way clustering. 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 clusterscluster_time
- Boolean indicating to use time 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.
Examples
>>> from linearmodels import RandomEffects >>> mod = RandomEffects(y, x) >>> res = mod.fit(cov_type="clustered", cluster_entity=True)
- Return type: