Robust Standard Errors: Poisson Panel Regression (pglm, lmtest)
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As a non-statistician I reached my limit here:
I try to fit a Poisson model for panel data (using pglm) and I want to calculate robust standard errors (using lmtest).
My code currently looks like this:
#poisson model (panel with year fixed effects):
poisson_model <- pglm(y ~ a + b + c + factor(year), data = regression_data,
model = "pooling", family = poisson, index = c("ID", "year"))
#robust standard errors:
robust_SE_model <- coeftest(poisson_model, vcov. = vcovHC(poisson_model, type = "HC1"))
This code works fine for one of my other model specifications when I fit a regular panel model with plm, but when I try the poisson model with pglm I receive the following error message:
Error in terms.default(object) : no terms component nor attribute
Is this due to a limitation of the lmtest package or am I making a mistake here? I really hope I can solve the problem using packages (not necessarily pglm and lmtest) and don't have to dive into manual calculation of robust errors.
Any help is highly appreciated!
r panel poisson robust
asked Nov 9 at 10:53
sspade
213
add a comment |
up vote
0
down vote
favorite
As a non-statistician I reached my limit here:
I try to fit a Poisson model for panel data (using pglm) and I want to calculate robust standard errors (using lmtest).
My code currently looks like this:
#poisson model (panel with year fixed effects):
poisson_model <- pglm(y ~ a + b + c + factor(year), data = regression_data,
model = "pooling", family = poisson, index = c("ID", "year"))
#robust standard errors:
robust_SE_model <- coeftest(poisson_model, vcov. = vcovHC(poisson_model, type = "HC1"))
This code works fine for one of my other model specifications when I fit a regular panel model with plm, but when I try the poisson model with pglm I receive the following error message:
Error in terms.default(object) : no terms component nor attribute
Is this due to a limitation of the lmtest package or am I making a mistake here? I really hope I can solve the problem using packages (not necessarily pglm and lmtest) and don't have to dive into manual calculation of robust errors.
Any help is highly appreciated!
r panel poisson robust
asked Nov 9 at 10:53
sspade
213
1
As far as I can decipher from?pglm, the library does not support sandwich error matrices. cran.r-project.org/web/packages/poisFErobust/index.html or cran.r-project.org/web/packages/glmmML might be useful.
– Otto Kässi
Nov 9 at 12:15
@OttoKässi Thanks, that was indeed the problem. The packages you mention will help me for sure. The whole robust errors for models which are non-linear in the parameters topic is a highly controversial one, which I was not completely aware of so far. So applying robust errors if there is heteroscedasticity does not cure any problems obviously: davegiles.blogspot.com/2013/05/…
– sspade
Nov 22 at 9:29
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
As a non-statistician I reached my limit here:
I try to fit a Poisson model for panel data (using pglm) and I want to calculate robust standard errors (using lmtest).
My code currently looks like this:
#poisson model (panel with year fixed effects):
poisson_model <- pglm(y ~ a + b + c + factor(year), data = regression_data,
model = "pooling", family = poisson, index = c("ID", "year"))
#robust standard errors:
robust_SE_model <- coeftest(poisson_model, vcov. = vcovHC(poisson_model, type = "HC1"))
This code works fine for one of my other model specifications when I fit a regular panel model with plm, but when I try the poisson model with pglm I receive the following error message:
Error in terms.default(object) : no terms component nor attribute
Is this due to a limitation of the lmtest package or am I making a mistake here? I really hope I can solve the problem using packages (not necessarily pglm and lmtest) and don't have to dive into manual calculation of robust errors.
Any help is highly appreciated!
r panel poisson robust
asked Nov 9 at 10:53
sspade
213
As a non-statistician I reached my limit here:
I try to fit a Poisson model for panel data (using pglm) and I want to calculate robust standard errors (using lmtest).
My code currently looks like this:
#poisson model (panel with year fixed effects):
poisson_model <- pglm(y ~ a + b + c + factor(year), data = regression_data,
model = "pooling", family = poisson, index = c("ID", "year"))
#robust standard errors:
robust_SE_model <- coeftest(poisson_model, vcov. = vcovHC(poisson_model, type = "HC1"))
This code works fine for one of my other model specifications when I fit a regular panel model with plm, but when I try the poisson model with pglm I receive the following error message:
Error in terms.default(object) : no terms component nor attribute
Is this due to a limitation of the lmtest package or am I making a mistake here? I really hope I can solve the problem using packages (not necessarily pglm and lmtest) and don't have to dive into manual calculation of robust errors.
Any help is highly appreciated!
r panel poisson robust
r panel poisson robust
asked Nov 9 at 10:53
sspade
213
asked Nov 9 at 10:53
sspade
213
asked Nov 9 at 10:53
sspade
213
asked Nov 9 at 10:53
sspade
213
asked Nov 9 at 10:53
sspade
213
213
1
As far as I can decipher from?pglm, the library does not support sandwich error matrices. cran.r-project.org/web/packages/poisFErobust/index.html or cran.r-project.org/web/packages/glmmML might be useful.
– Otto Kässi
Nov 9 at 12:15
@OttoKässi Thanks, that was indeed the problem. The packages you mention will help me for sure. The whole robust errors for models which are non-linear in the parameters topic is a highly controversial one, which I was not completely aware of so far. So applying robust errors if there is heteroscedasticity does not cure any problems obviously: davegiles.blogspot.com/2013/05/…
– sspade
Nov 22 at 9:29
add a comment |
1
As far as I can decipher from?pglm, the library does not support sandwich error matrices. cran.r-project.org/web/packages/poisFErobust/index.html or cran.r-project.org/web/packages/glmmML might be useful.
– Otto Kässi
Nov 9 at 12:15
@OttoKässi Thanks, that was indeed the problem. The packages you mention will help me for sure. The whole robust errors for models which are non-linear in the parameters topic is a highly controversial one, which I was not completely aware of so far. So applying robust errors if there is heteroscedasticity does not cure any problems obviously: davegiles.blogspot.com/2013/05/…
– sspade
Nov 22 at 9:29
1
1
As far as I can decipher from
?pglm, the library does not support sandwich error matrices. cran.r-project.org/web/packages/poisFErobust/index.html or cran.r-project.org/web/packages/glmmML might be useful.– Otto Kässi
Nov 9 at 12:15
As far as I can decipher from
?pglm, the library does not support sandwich error matrices. cran.r-project.org/web/packages/poisFErobust/index.html or cran.r-project.org/web/packages/glmmML might be useful.– Otto Kässi
Nov 9 at 12:15
@OttoKässi Thanks, that was indeed the problem. The packages you mention will help me for sure. The whole robust errors for models which are non-linear in the parameters topic is a highly controversial one, which I was not completely aware of so far. So applying robust errors if there is heteroscedasticity does not cure any problems obviously: davegiles.blogspot.com/2013/05/…
– sspade
Nov 22 at 9:29
@OttoKässi Thanks, that was indeed the problem. The packages you mention will help me for sure. The whole robust errors for models which are non-linear in the parameters topic is a highly controversial one, which I was not completely aware of so far. So applying robust errors if there is heteroscedasticity does not cure any problems obviously: davegiles.blogspot.com/2013/05/…
– sspade
Nov 22 at 9:29
add a comment |
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As far as I can decipher from
?pglm, the library does not support sandwich error matrices. cran.r-project.org/web/packages/poisFErobust/index.html or cran.r-project.org/web/packages/glmmML might be useful.– Otto Kässi
Nov 9 at 12:15
@OttoKässi Thanks, that was indeed the problem. The packages you mention will help me for sure. The whole robust errors for models which are non-linear in the parameters topic is a highly controversial one, which I was not completely aware of so far. So applying robust errors if there is heteroscedasticity does not cure any problems obviously: davegiles.blogspot.com/2013/05/…
– sspade
Nov 22 at 9:29