vcov.femlm {FENmlm} | R Documentation |
This function extracts the variance-covariance of estimated parameters from a model estimated with femlm
.
## S3 method for class 'femlm' vcov(object, se = c("standard", "white", "cluster", "twoway", "threeway", "fourway"), cluster, dof_correction = FALSE, forceCovariance = FALSE, keepBounded = FALSE, ...)
object |
A femlm object. Obtained using |
se |
Character scalar. Which kind of standard error should be computed: “standard” (default), “White”, “cluster”, “twoway”, “threeway” or “fourway”? |
cluster |
A list of vectors. Used only if |
dof_correction |
Logical, default is |
forceCovariance |
(Advanced users.) Logical, default is |
keepBounded |
(Advanced users.) Logical, default is |
... |
Other arguments to be passed to The computation of the VCOV matrix is first done in |
It returns a N\times N square matrix where N is the number of variables of the fitted model. This matrix has an attribute “type” specifying how this variance/covariance matrix has been commputed (i.e. was it created using White correction, or was it clustered along a specific factor, etc).
Laurent Berge
femlm
, summary.femlm
, confint.femlm
, resid.femlm
, predict.femlm
, getFE
.
# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est_pois = femlm(Euros ~ log(dist_km) + log(Year) | Origin + Destination + Product, trade) # "normal" VCOV vcov(est_pois) # "white" VCOV vcov(est_pois, se = "white") # "clustered" VCOV (with respect to the Origin factor) vcov(est_pois, se = "cluster") # "clustered" VCOV (with respect to the Product factor) vcov(est_pois, se = "cluster", cluster = trade$Product) # another way to make the same request: vcov(est_pois, se = "cluster", cluster = "Product") # Another estimation without cluster: est_pois_simple = femlm(Euros ~ log(dist_km) + log(Year), trade) # We can still get the clustered VCOV, # but we need to give the cluster-vector: vcov(est_pois_simple, se = "cluster", cluster = trade$Product)