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.
id
a factor with levels
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
day
a numeric vector
fe
a numeric vector
x1
a numeric vector
x2
a numeric vector
y
a numeric vector
x1.lead
a 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.
id
a factor with levels
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
day
a numeric vector
fe
a numeric vector
x1
a numeric vector
x2
a numeric vector
y
a 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)