| Type: | Package |
| Title: | Poisson Fixed Effects Robust |
| Version: | 2.0.0 |
| Date: | 2020-02-17 |
| Description: | Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999). |
| License: | MIT + file LICENSE |
| Depends: | R (≥ 3.1.0) |
| Imports: | data.table (≥ 1.9.6), glmmML (≥ 1.0) |
| URL: | https://bitbucket.org/ew-btb/poisson-fe-robust |
| NeedsCompilation: | no |
| RoxygenNote: | 6.0.1 |
| Suggests: | testthat |
| LazyData: | true |
| Packaged: | 2020-02-17 20:28:47 UTC; evan |
| Author: | Evan Wright [aut, cre] |
| Maintainer: | Evan Wright <enwright@umich.edu> |
| Repository: | CRAN |
| Date/Publication: | 2020-02-17 21:40:06 UTC |
Poisson Fixed Effects Robust
Description
Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999).
Details
The DESCRIPTION file:
| Package: | poisFErobust |
| Type: | Package |
| Title: | Poisson Fixed Effects Robust |
| Version: | 2.0.0 |
| Date: | 2020-02-17 |
| Authors@R: | person("Evan", "Wright", email = "enwright@umich.edu", role = c("aut", "cre")) |
| Description: | Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999). |
| License: | MIT + file LICENSE |
| Depends: | R (>= 3.1.0) |
| Imports: | data.table (>= 1.9.6), glmmML (>= 1.0) |
| URL: | https://bitbucket.org/ew-btb/poisson-fe-robust |
| NeedsCompilation: | no |
| RoxygenNote: | 6.0.1 |
| Suggests: | testthat |
| LazyData: | true |
| Author: | Evan Wright [aut, cre] |
| Maintainer: | Evan Wright <enwright@umich.edu> |
Index of help topics:
ex.dt.bad Poisson data violating conditional mean
assumption
ex.dt.good Poisson data satisfying conditional mean
assumption
pois.fe.robust Robust standard errors of Poisson fixed effects
regression
poisFErobust-package Poisson Fixed Effects Robust
Author(s)
NA
Maintainer: NA
References
Wooldridge, Jeffrey M. (1999): "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, 90, 77-97.
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)
Poisson data violating conditional mean assumption
Description
A data.table containing id by day observations of Poisson
random variables which violate the conditional mean assumption of
Wooldridge (1999).
Usage
data("ex.dt.bad")Format
A data.table with 450 observations on the following 7 variables.
ida factor with levels
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950daya numeric vector
fea numeric vector
x1a numeric vector
x2a numeric vector
ya numeric vector
x1.leada numeric vector
Details
The data were simulated like
y <- rpois(1, exp(fe + x1 + x2 + 2.5*x1.lead))
where fe, x1, and x2 are standard normal random variables.
fe varies only across id.
x1.lead is a one period lead of x1 which causes the violation
of the conditional mean assumption.
References
Wooldridge, Jeffrey M. (1999): "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, 90, 77-97.
Examples
data("ex.dt.bad")
str(ex.dt.bad)
Poisson data satisfying conditional mean assumption
Description
A data.table containing id by day observations of Poisson
random variables which satisfy the conditional mean assumption of
Wooldridge (1999).
Usage
data("ex.dt.good")Format
A data frame with 500 observations on the following 6 variables.
ida factor with levels
1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950daya numeric vector
fea numeric vector
x1a numeric vector
x2a numeric vector
ya numeric vector
Details
The data were simulated like
y <- rpois(1, exp(fe + x1 + x2))
where fe, x1, and x2 are standard normal random variables.
fe varies only across id.
References
Wooldridge, Jeffrey M. (1999): "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, 90, 77-97.
Examples
data("ex.dt.good")
str(ex.dt.good)
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)