The function `waldtest`

computes a Wald test for the H0: R beta = r,
where beta is the estimated vector `coef(object)`

.If `R`

is a character, integer, or logical vector it is assumed to
specify a matrix which merely picks out a subset of the coefficients for
joint testing. If `r`

is not specified, it is assumed to be a zero
vector of the appropriate length.

`R`

can also be a formula which is linear in the estimated
coefficients, e.g. of the type `~Q-2|x-2*z`

which will test the joint
hypothesis Q=2 and x=2*z.

If `R`

is a function (of the coefficients), an approximate Wald test
against H0: `R(beta) == 0`

, using the Delta-method, is computed.

In case of an IV-estimation, the names for the endogenous variables in
`coef(object)`

are of the type `"`Q(fit)`"`

which is a bit dull to
type; if all the endogenous variables are to be tested they can be specified
as `"endovars"`

. It is also possible to specify an endogenous variable
simply as `"Q"`

, and `waldtest`

will add the other syntactic sugar
to obtain `"`Q(fit)`"`

.

The `type`

argument works as follows. If `type=='default'`

it is
assumed that the residuals are i.i.d., unless a cluster structure was
specified to `felm`

. If `type=='robust'`

, a heteroscedastic
structure is assumed, even if a cluster structure was specified in
`felm`

.