plm: Panel Data Estimators
In plm: Linear Models for Panel Data
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Linear models for panel data estimated using the lm function on
transformed data.
Usage
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plm(
formula,
data,
subset,
weights,
na.action,
effect = c("individual", "time", "twoways", "nested"),
model = c("within", "random", "ht", "between", "pooling", "fd"),
random.method = NULL,
random.models = NULL,
random.dfcor = NULL,
inst.method = c("bvk", "baltagi", "am", "bms"),
restrict.matrix = NULL,
restrict.rhs = NULL,
index = NULL,
...
)
## S3 method for class 'plm.list'
print(
x,
digits = max(3, getOption("digits") - 2),
width = getOption("width"),
...
)
## S3 method for class 'panelmodel'
terms(x, ...)
## S3 method for class 'panelmodel'
vcov(object, ...)
## S3 method for class 'panelmodel'
fitted(object, ...)
## S3 method for class 'panelmodel'
residuals(object, ...)
## S3 method for class 'panelmodel'
df.residual(object, ...)
## S3 method for class 'panelmodel'
coef(object, ...)
## S3 method for class 'panelmodel'
print(
x,
digits = max(3, getOption("digits") - 2),
width = getOption("width"),
...
)
## S3 method for class 'panelmodel'
update(object, formula., ..., evaluate = TRUE)
## S3 method for class 'panelmodel'
deviance(object, model = NULL, ...)
## S3 method for class 'plm'
predict(object, newdata = NULL, ...)
## S3 method for class 'plm'
formula(x, ...)
## S3 method for class 'plm'
plot(
x,
dx = 0.2,
N = NULL,
seed = 1,
within = TRUE,
pooling = TRUE,
between = FALSE,
random = FALSE,
...
)
## S3 method for class 'plm'
residuals(object, model = NULL, effect = NULL, ...)
## S3 method for class 'plm'
fitted(object, model = NULL, effect = NULL, ...)
Arguments
formula
a symbolic description for the model to be
estimated,
data
a data.frame,
subset
see stats::lm(),
weights
see stats::lm(),
na.action
see stats::lm(); currently, not fully
supported,
effect
the effects introduced in the model, one of
"individual", "time", "twoways", or
"nested",
model
one of "pooling", "within",
"between", "random" "fd", or "ht",
random.method
method of estimation for the variance
components in the random effects model, one of "swar"
(default), "amemiya", "walhus", "nerlove"; for
Hausman-Taylor estimation set to "ht" (see Details and Examples),
random.models
an alternative to the previous argument, the
models used to compute the variance components estimations are
indicated,
random.dfcor
a numeric vector of length 2 indicating which
degree of freedom should be used,
inst.method
the instrumental variable transformation: one of
"bvk", "baltagi", "am", or "bms" (see also Details),
restrict.matrix
a matrix which defines linear restrictions
on the coefficients,
restrict.rhs
the right hand side vector of the linear
restrictions on the coefficients,
index
the indexes,
...
further arguments.
x, object
an object of class "plm",
digits
number of digits for printed output,
width
the maximum length of the lines in the printed output,
formula.
a new formula for the update method,
evaluate
a boolean for the update method, if TRUE the
updated model is returned, if FALSE the call is returned,
newdata
the new data set for the predict method,
dx
the half–length of the individual lines for the plot
method (relative to x range),
N
the number of individual to plot,
seed
the seed which will lead to individual selection,
within
if TRUE, the within model is plotted,
pooling
if TRUE, the pooling model is plotted,
between
if TRUE, the between model is plotted,
random
if TRUE, the random effect model is plotted,
Details
plm is a general function for the estimation of linear panel
models. It supports the following estimation methods: pooled OLS
(model = "pooling"), fixed effects ("within"), random effects
("random"), first–differences ("fd"), and between
("between"). It supports unbalanced panels and two–way effects
(although not with all methods).
For random effects models, four estimators of the transformation
parameter are available by setting random.method to one of
"swar" \insertCiteSWAM:AROR:72plm (default), "amemiya"
\insertCiteAMEM:71plm, "walhus"
\insertCiteWALL:HUSS:69plm, or "nerlove"
\insertCiteNERLO:71plm (see below for Hausman-Taylor instrumental
variable case).
For first–difference models, the intercept is maintained (which
from a specification viewpoint amounts to allowing for a trend in
the levels model). The user can exclude it from the estimated
specification the usual way by adding "-1" to the model formula.
Instrumental variables estimation is obtained using two–part
formulas, the second part indicating the instrumental variables
used. This can be a complete list of instrumental variables or an
update of the first part. If, for example, the model is y ~ x1 + x2 + x3, with x1 and x2 endogenous and z1 and z2 external
instruments, the model can be estimated with:
-
formula = y~x1+x2+x3 | x3+z1+z2,
-
formula = y~x1+x2+x3 | . -x1-x2+z1+z2.
If an instrument variable estimation is requested, argument
inst.method selects the instrument variable transformation
method:
-
"bvk" (default) for \insertCiteBALE:VARA:87;textualplm,
-
"baltagi" for \insertCiteBALT:81;textualplm,
-
"am" for \insertCiteAMEM:MACU:86;textualplm,
-
"bms" for \insertCiteBREU:MIZO:SCHM:89;textualplm.
The Hausman–Taylor estimator \insertCiteHAUS:TAYL:81plm is
computed with arguments random.method = "ht", model = "random",
inst.method = "baltagi" (the other way with only model = "ht"
is deprecated).
See also the vignettes for introductions to model estimations (and more) with
examples.
Value
An object of class "plm".
A "plm" object has the following elements :
coefficients
the vector of coefficients,
vcov
the variance–covariance matrix of the coefficients,
residuals
the vector of residuals (these are the residuals
of the (quasi-)demeaned model),
weights
(only for weighted estimations) weights as
specified,
df.residual
degrees of freedom of the residuals,
formula
an object of class "pFormula" describing the model,
model
the model frame as a "pdata.frame" containing the
variables used for estimation: the response is in first column followed by
the other variables, the individual and time indexes are in the 'index'
attribute of model,
ercomp
an object of class "ercomp" providing the
estimation of the components of the errors (for random effects
models only),
aliased
named logical vector indicating any aliased
coefficients which are silently dropped by plm due to
linearly dependent terms (see also detect.lindep()),
call
the call.
It has print, summary and print.summary methods. The
summary method creates an object of class "summary.plm" that
extends the object it is run on with information about (inter alia) F
statistic and (adjusted) R-squared of model, standard errors, t–values, and
p–values of coefficients, (if supplied) the furnished vcov, see
summary.plm() for further details.
Author(s)
Yves Croissant
References
\insertRefAMEM:71plm
\insertRefAMEM:MACU:86plm
\insertRefBALE:VARA:87plm
\insertRefBALT:81plm
\insertRefBALT:SONG:JUNG:01plm
\insertRefBALT:13plm
\insertRefBREU:MIZO:SCHM:89plm
\insertRefHAUS:TAYL:81plm
\insertRefNERLO:71plm
\insertRefSWAM:AROR:72plm
\insertRefWALL:HUSS:69plm
See Also
summary.plm() for further details about the associated
summary method and the "summary.plm" object both of which provide some model
tests and tests of coefficients. fixef() to compute the fixed
effects for "within" models (=fixed effects models).
Examples
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data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state","year"))
summary(zz)
# replicates some results from Baltagi (2013), table 3.1
data("Grunfeld", package = "plm")
p <- plm(inv ~ value + capital,
data = Grunfeld, model = "pooling")
wi <- plm(inv ~ value + capital,
data = Grunfeld, model = "within", effect = "twoways")
swar <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", effect = "twoways")
amemiya <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "amemiya",
effect = "twoways")
walhus <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "walhus",
effect = "twoways")
# summary and summary with a furnished vcov (passed as matrix,
# as function, and as function with additional argument)
summary(wi)
summary(wi, vcov = vcovHC(wi))
summary(wi, vcov = vcovHC)
summary(wi, vcov = function(x) vcovHC(x, method = "white2"))
## nested random effect model
# replicate Baltagi/Song/Jung (2001), p. 378 (table 6), columns SA, WH
# == Baltagi (2013), pp. 204-205
data("Produc", package = "plm")
pProduc <- pdata.frame(Produc, index = c("state", "year", "region"))
form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp
summary(plm(form, data = pProduc, model = "random", effect = "nested"))
summary(plm(form, data = pProduc, model = "random", effect = "nested",
random.method = "walhus"))
## Instrumental variable estimations
# replicate Baltagi (2013/2021), p. 133/162, table 7.1
data("Crime", package = "plm")
FE2SLS <- plm(lcrmrte ~ lprbarr + lpolpc + lprbconv + lprbpris + lavgsen +
ldensity + lwcon + lwtuc + lwtrd + lwfir + lwser + lwmfg + lwfed +
lwsta + lwloc + lpctymle + lpctmin + region + smsa + factor(year)
| . - lprbarr - lpolpc + ltaxpc + lmix,
data = Crime, model = "within")
G2SLS <- update(FE2SLS, model = "random", inst.method = "bvk")
EC2SLS <- update(G2SLS, model = "random", inst.method = "baltagi")
## Hausman-Taylor estimator and Amemiya-MaCurdy estimator
# replicate Baltagi (2005, 2013), table 7.4; Baltagi (2021), table 7.5
data("Wages", package = "plm")
ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + union + exp + I(exp ^ 2),
data = Wages, index = 595,
random.method = "ht", model = "random", inst.method = "baltagi")
summary(ht)
am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + union + exp + I(exp ^ 2),
data = Wages, index = 595,
random.method = "ht", model = "random", inst.method = "am")
summary(am)
plm documentation built on Sept. 21, 2021, 3:01 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Linear models for panel data estimated using the lm function on
transformed data.
Usage
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | plm(
formula,
data,
subset,
weights,
na.action,
effect = c("individual", "time", "twoways", "nested"),
model = c("within", "random", "ht", "between", "pooling", "fd"),
random.method = NULL,
random.models = NULL,
random.dfcor = NULL,
inst.method = c("bvk", "baltagi", "am", "bms"),
restrict.matrix = NULL,
restrict.rhs = NULL,
index = NULL,
...
)
## S3 method for class 'plm.list'
print(
x,
digits = max(3, getOption("digits") - 2),
width = getOption("width"),
...
)
## S3 method for class 'panelmodel'
terms(x, ...)
## S3 method for class 'panelmodel'
vcov(object, ...)
## S3 method for class 'panelmodel'
fitted(object, ...)
## S3 method for class 'panelmodel'
residuals(object, ...)
## S3 method for class 'panelmodel'
df.residual(object, ...)
## S3 method for class 'panelmodel'
coef(object, ...)
## S3 method for class 'panelmodel'
print(
x,
digits = max(3, getOption("digits") - 2),
width = getOption("width"),
...
)
## S3 method for class 'panelmodel'
update(object, formula., ..., evaluate = TRUE)
## S3 method for class 'panelmodel'
deviance(object, model = NULL, ...)
## S3 method for class 'plm'
predict(object, newdata = NULL, ...)
## S3 method for class 'plm'
formula(x, ...)
## S3 method for class 'plm'
plot(
x,
dx = 0.2,
N = NULL,
seed = 1,
within = TRUE,
pooling = TRUE,
between = FALSE,
random = FALSE,
...
)
## S3 method for class 'plm'
residuals(object, model = NULL, effect = NULL, ...)
## S3 method for class 'plm'
fitted(object, model = NULL, effect = NULL, ...)
|
Arguments
formula |
a symbolic description for the model to be estimated, |
data |
a |
subset |
see |
weights |
see |
na.action |
see |
effect |
the effects introduced in the model, one of
|
model |
one of |
random.method |
method of estimation for the variance
components in the random effects model, one of |
random.models |
an alternative to the previous argument, the models used to compute the variance components estimations are indicated, |
random.dfcor |
a numeric vector of length 2 indicating which degree of freedom should be used, |
inst.method |
the instrumental variable transformation: one of
|
restrict.matrix |
a matrix which defines linear restrictions on the coefficients, |
restrict.rhs |
the right hand side vector of the linear restrictions on the coefficients, |
index |
the indexes, |
... |
further arguments. |
x, object |
an object of class |
digits |
number of digits for printed output, |
width |
the maximum length of the lines in the printed output, |
formula. |
a new formula for the update method, |
evaluate |
a boolean for the update method, if |
newdata |
the new data set for the |
dx |
the half–length of the individual lines for the plot method (relative to x range), |
N |
the number of individual to plot, |
seed |
the seed which will lead to individual selection, |
within |
if |
pooling |
if |
between |
if |
random |
if |
Details
plm is a general function for the estimation of linear panel
models. It supports the following estimation methods: pooled OLS
(model = "pooling"), fixed effects ("within"), random effects
("random"), first–differences ("fd"), and between
("between"). It supports unbalanced panels and two–way effects
(although not with all methods).
For random effects models, four estimators of the transformation
parameter are available by setting random.method to one of
"swar" \insertCiteSWAM:AROR:72plm (default), "amemiya"
\insertCiteAMEM:71plm, "walhus"
\insertCiteWALL:HUSS:69plm, or "nerlove"
\insertCiteNERLO:71plm (see below for Hausman-Taylor instrumental
variable case).
For first–difference models, the intercept is maintained (which
from a specification viewpoint amounts to allowing for a trend in
the levels model). The user can exclude it from the estimated
specification the usual way by adding "-1" to the model formula.
Instrumental variables estimation is obtained using two–part
formulas, the second part indicating the instrumental variables
used. This can be a complete list of instrumental variables or an
update of the first part. If, for example, the model is y ~ x1 + x2 + x3, with x1 and x2 endogenous and z1 and z2 external
instruments, the model can be estimated with:
-
formula = y~x1+x2+x3 | x3+z1+z2, -
formula = y~x1+x2+x3 | . -x1-x2+z1+z2.
If an instrument variable estimation is requested, argument
inst.method selects the instrument variable transformation
method:
-
"bvk"(default) for \insertCiteBALE:VARA:87;textualplm, -
"baltagi"for \insertCiteBALT:81;textualplm, -
"am"for \insertCiteAMEM:MACU:86;textualplm, -
"bms"for \insertCiteBREU:MIZO:SCHM:89;textualplm.
The Hausman–Taylor estimator \insertCiteHAUS:TAYL:81plm is
computed with arguments random.method = "ht", model = "random",
inst.method = "baltagi" (the other way with only model = "ht"
is deprecated).
See also the vignettes for introductions to model estimations (and more) with examples.
Value
An object of class "plm".
A "plm" object has the following elements :
coefficients |
the vector of coefficients, |
vcov |
the variance–covariance matrix of the coefficients, |
residuals |
the vector of residuals (these are the residuals of the (quasi-)demeaned model), |
weights |
(only for weighted estimations) weights as specified, |
df.residual |
degrees of freedom of the residuals, |
formula |
an object of class |
model |
the model frame as a |
ercomp |
an object of class |
aliased |
named logical vector indicating any aliased
coefficients which are silently dropped by |
call |
the call. |
It has print, summary and print.summary methods. The
summary method creates an object of class "summary.plm" that
extends the object it is run on with information about (inter alia) F
statistic and (adjusted) R-squared of model, standard errors, t–values, and
p–values of coefficients, (if supplied) the furnished vcov, see
summary.plm() for further details.
Author(s)
Yves Croissant
References
\insertRefAMEM:71plm
\insertRefAMEM:MACU:86plm
\insertRefBALE:VARA:87plm
\insertRefBALT:81plm
\insertRefBALT:SONG:JUNG:01plm
\insertRefBALT:13plm
\insertRefBREU:MIZO:SCHM:89plm
\insertRefHAUS:TAYL:81plm
\insertRefNERLO:71plm
\insertRefSWAM:AROR:72plm
\insertRefWALL:HUSS:69plm
See Also
summary.plm() for further details about the associated
summary method and the "summary.plm" object both of which provide some model
tests and tests of coefficients. fixef() to compute the fixed
effects for "within" models (=fixed effects models).
Examples
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state","year"))
summary(zz)
# replicates some results from Baltagi (2013), table 3.1
data("Grunfeld", package = "plm")
p <- plm(inv ~ value + capital,
data = Grunfeld, model = "pooling")
wi <- plm(inv ~ value + capital,
data = Grunfeld, model = "within", effect = "twoways")
swar <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", effect = "twoways")
amemiya <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "amemiya",
effect = "twoways")
walhus <- plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "walhus",
effect = "twoways")
# summary and summary with a furnished vcov (passed as matrix,
# as function, and as function with additional argument)
summary(wi)
summary(wi, vcov = vcovHC(wi))
summary(wi, vcov = vcovHC)
summary(wi, vcov = function(x) vcovHC(x, method = "white2"))
## nested random effect model
# replicate Baltagi/Song/Jung (2001), p. 378 (table 6), columns SA, WH
# == Baltagi (2013), pp. 204-205
data("Produc", package = "plm")
pProduc <- pdata.frame(Produc, index = c("state", "year", "region"))
form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp
summary(plm(form, data = pProduc, model = "random", effect = "nested"))
summary(plm(form, data = pProduc, model = "random", effect = "nested",
random.method = "walhus"))
## Instrumental variable estimations
# replicate Baltagi (2013/2021), p. 133/162, table 7.1
data("Crime", package = "plm")
FE2SLS <- plm(lcrmrte ~ lprbarr + lpolpc + lprbconv + lprbpris + lavgsen +
ldensity + lwcon + lwtuc + lwtrd + lwfir + lwser + lwmfg + lwfed +
lwsta + lwloc + lpctymle + lpctmin + region + smsa + factor(year)
| . - lprbarr - lpolpc + ltaxpc + lmix,
data = Crime, model = "within")
G2SLS <- update(FE2SLS, model = "random", inst.method = "bvk")
EC2SLS <- update(G2SLS, model = "random", inst.method = "baltagi")
## Hausman-Taylor estimator and Amemiya-MaCurdy estimator
# replicate Baltagi (2005, 2013), table 7.4; Baltagi (2021), table 7.5
data("Wages", package = "plm")
ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + union + exp + I(exp ^ 2),
data = Wages, index = 595,
random.method = "ht", model = "random", inst.method = "baltagi")
summary(ht)
am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) +
bluecol + ind + union + sex + black + ed |
bluecol + south + smsa + ind + sex + black |
wks + married + union + exp + I(exp ^ 2),
data = Wages, index = 595,
random.method = "ht", model = "random", inst.method = "am")
summary(am)
|
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