object of class "felm", a result of a call to
felm.
R
matrix, character, formula, function, integer or logical.
Specification of which exclusions to test.
r
numerical vector.
type
character. Error structure type.
lhs
character. Name of left hand side if multiple left hand sides.
df1
integer. If you know better than the default df, specify it here.
df2
integer. If you know better than the default df, specify it here.
Details
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.
Value
The function waldtest computes and returns a named numeric
vector containing the following elements.
p is the p-value for the Chi^2-test
chi2
is the Chi^2-distributed statistic.
df1 is the degrees of
freedom for the Chi^2 statistic.
p.F is the p-value for the F
statistics
F is the F-distributed statistic.
df2
is the additional degrees of freedom for the F statistic.
The return value has an attribute 'formula' which encodes the
restrictions.
See Also
nlexpect
Examples
x <- rnorm(10000)
x2 <- rnorm(length(x))
y <- x - 0.2*x2 + rnorm(length(x))
#Also works for lm
summary(est <- lm(y ~ x + x2 ))
# We do not reject the true values
waldtest(est, ~ x-1|x2+0.2|`(Intercept)`)
# The Delta-method coincides when the function is linear:
waldtest(est, function(x) x - c(0, 1, -0.2))