poisFErobust (version 2.0.0)

pois.fe.robust: 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 xvars. If NULL, coefficients are estimated using glmmML::glmmboot.

allow.set.key

logical. When TRUE (recommended), data will have its key set to group.name, so it may be reordered. This should reduce memory usage.

index.name

DEPRECATED (leave as NULL).

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).

Details

data must be a data.table containing the following:

• a column named by outcome, non-negative integer

• columns named according to each string in xvars, numeric type

• a column named by group.name, factor type

• a 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.

References

Wooldridge, Jeffrey M. (1999): "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, 90, 77-97.

See Also

glmmboot

Examples

# NOT RUN {
# 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)
# }