pois.fe.robust {poisFErobust} | R Documentation |
Robust standard errors of Poisson fixed effects regression
Description
Compute standard errors following Wooldridge (1999) for Poisson regression with fixed effects, and a hypothesis test of the conditional mean assumption (3.1).
Usage
pois.fe.robust(outcome, xvars, group.name, data,
qcmle.coefs = NULL, allow.set.key = FALSE,
index.name = NULL)
Arguments
outcome |
character string of the name of the dependent variable. |
xvars |
vector of character strings of the names of the independent variables. |
group.name |
character string of the name of the grouping variable. |
data |
data.table which contains the variables named in other arguments. See details for variable type requirements. |
qcmle.coefs |
an optional numeric vector of coefficients in the same order as |
allow.set.key |
logical. When |
index.name |
DEPRECATED (leave as NULL). |
Details
data
must be a data.table
containing the following:
a column named by
outcome
, non-negative integercolumns named according to each string in
xvars
, numeric typea column named by
group.name
, factor typea column named by
index.name
, integer sequence increasing by one each observation with no gaps within groups
No observation in data
may contain a missing value.
Setting allow.set.key
to TRUE
is recommended to reduce
memory usage; however, it will allow data
to be modified
(sorted in-place).
pois.fe.robust
also returns the p-value of the hypothesis test of the
conditional mean assumption (3.1) as described in Wooldridge (1999) section 3.3.
Value
A list containing
coefficients
, a numeric vector of coefficients.se.robust
, a numeric vector of standard errors.p.value
, the p-value of a hypothesis test of the conditional mean assumption (3.1).
Author(s)
Evan Wright
References
Wooldridge, Jeffrey M. (1999): "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, 90, 77-97.
See Also
Examples
# ex.dt.good satisfies the conditional mean assumption
data("ex.dt.good")
pois.fe.robust(outcome = "y", xvars = c("x1", "x2"), group.name = "id",
index.name = "day", data = ex.dt.good)
# ex.dt.bad violates the conditional mean assumption
data("ex.dt.bad")
pois.fe.robust(outcome = "y", xvars = c("x1", "x2"), group.name = "id",
index.name = "day", data = ex.dt.bad)