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Generalized method of moments estimation for static or dynamic models with panel data.
pgmm(formula, data, subset, na.action, effect = c("twoways",
"individual"), model = c("onestep", "twosteps"), collapse = FALSE,
lost.ts = NULL, transformation = c("d", "ld"), fsm = NULL,
index = NULL, ...)# S3 method for pgmm
coef(object, ...)
# S3 method for pgmm
summary(object, robust = TRUE, time.dummies = FALSE,
...)
# S3 method for summary.pgmm
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
a symbolic description for the model to be estimated. The preferred interface is now to indicate a multi--part formula, the first two parts describing the covariates and the GMM instruments and, if any, the third part the 'normal' instruments,
a data.frame
(neither factors nor character vectors
will be accepted in data.frame
),
see lm()
,
see lm()
,
the effects introduced in the model, one of
"twoways"
(the default) or "individual"
,
one of "onestep"
(the default) or "twosteps"
,
if TRUE
, the GMM instruments are collapsed,
the number of lost time series: if NULL
, this is
automatically computed. Otherwise, it can be defined by the
user as a numeric vector of length 1 or 2. The first element is
the number of lost time series in the model in difference, the
second one in the model in level. If the second element is
missing, it is set to the first one minus one,
the kind of transformation to apply to the
model: either "d"
(the default value) for the
"difference GMM" model or "ld"
for the "system GMM",
the matrix for the one step estimator: one of "I"
(identity matrix) or "G"
(transformation="d"
, one of
"GI"
or "full"
if transformation="ld"
,
the indexes,
further arguments.
an object of class "pgmm"
,
if TRUE
, robust inference is performed in the
summary,
if TRUE
, the estimated coefficients of time
dummies are present in the table of coefficients,
digits,
the maximum length of the lines in the print output,
An object of class c("pgmm","panelmodel")
, which has the
following elements:
the vector (or the list for fixed effects) of coefficients,
the vector of residuals,
the covariance matrix of the coefficients,
the vector of fitted values,
degrees of freedom of the residuals,
a list containing the variables used for the estimation for each individual,
a list containing the instruments for each individual (two lists in case of "sys--GMM"),
the weighting matrix for the one--step estimator,
the weighting matrix for the two--steps estimator,
the call.
It has print, summary and print.summary methods.
pgmm
estimates a model for panel data with a generalized method
of moments (GMM) estimator. The description of the model to
estimate is provided with a multi--part formula which is (or which
is coerced to) a Formula
object. The first right--hand side part
describes the covariates. The second one, which is mandatory,
describes the GMM instruments. The third one, which is optional,
describes the 'normal' instruments. By default, all the variables
of the model which are not used as GMM instruments are used as
normal instruments with the same lag structure as the one specified
in the model.
y~lag(y, 1:2)+lag(x1, 0:1)+lag(x2, 0:2) | lag(y, 2:99)
is similar to
y~lag(y, 1:2)+lag(x1, 0:1)+lag(x2, 0:2) | lag(y, 2:99) | lag(x1, 0:1)+lag(x2, 0:2)
and indicates that all lags from 2 of y
are used
as GMM instruments.
transformation
indicates how the model should be transformed for
the estimation. "d"
gives the "difference GMM" model
@see @AREL:BOND:91plm, "ld"
the "system GMM" model
@see @BLUN:BOND:98plm.
pgmm
is an attempt to adapt GMM estimators available within the
DPD library for GAUSS @see @AREL:BOND:98plm and Ox
@see @DOOR:AREL:BOND:12plm and within the xtabond2
library for Stata @see @ROOD:09plm.
sargan()
for the Hansen--Sargan test and mtest()
for
Arellano--Bond's test of serial correlation. dynformula()
for
dynamic formulas (deprecated).
# NOT RUN {
data("EmplUK", package = "plm")
## Arellano and Bond (1991), table 4 col. b
z1 <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1)
+ log(capital) + lag(log(output), 0:1) | lag(log(emp), 2:99),
data = EmplUK, effect = "twoways", model = "twosteps")
summary(z1, robust = FALSE)
## Blundell and Bond (1998) table 4 (cf. DPD for OX p. 12 col. 4)
z2 <- pgmm(log(emp) ~ lag(log(emp), 1)+ lag(log(wage), 0:1) +
lag(log(capital), 0:1) | lag(log(emp), 2:99) +
lag(log(wage), 2:99) + lag(log(capital), 2:99),
data = EmplUK, effect = "twoways", model = "onestep",
transformation = "ld")
summary(z2, robust = TRUE)
# }
# NOT RUN {
## Same with the old formula or dynformula interface
## Arellano and Bond (1991), table 4, col. b
z1 <- pgmm(log(emp) ~ log(wage) + log(capital) + log(output),
lag.form = list(2,1,0,1), data = EmplUK,
effect = "twoways", model = "twosteps",
gmm.inst = ~log(emp), lag.gmm = list(c(2,99)))
summary(z1, robust = FALSE)
## Blundell and Bond (1998) table 4 (cf DPD for OX p. 12 col. 4)
z2 <- pgmm(dynformula(log(emp) ~ log(wage) + log(capital), list(1,1,1)),
data = EmplUK, effect = "twoways", model = "onestep",
gmm.inst = ~log(emp) + log(wage) + log(capital),
lag.gmm = c(2,99), transformation = "ld")
summary(z2, robust = TRUE)
# }
# NOT RUN {
# }
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