plm  R Documentation 
Linear models for panel data estimated using the lm
function on
transformed data.
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' 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, ...)
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 
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 
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 HausmanTaylor 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  . x1x2+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.
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) Rsquared of model, standard errors, t–values, and
p–values of coefficients, (if supplied) the furnished vcov, see
summary.plm()
for further details.
Yves Croissant
AMEM: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
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). predict.plm()
for
predicted values.
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. 204205 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") ## HausmanTaylor estimator and AmemiyaMaCurdy 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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