Description Usage Arguments Methods Examples
Method to fit a model using GMM, from an object of class "gmmModels"
.
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  ## S4 method for signature 'gmmModels'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, theta0=NULL, ...)
## S4 method for signature 'formulaGmm'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, theta0=NULL, ...)
## S4 method for signature 'sysGmmModels'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls", "EbyE"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, theta0=NULL, EbyE=FALSE, ...)
## S4 method for signature 'rnonlinearGmm'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, theta0=NULL, ...)
## S4 method for signature 'rlinearGmm'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, ...)
## S4 method for signature 'rformulaGmm'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, theta0=NULL, ...)
## S4 method for signature 'rslinearGmm'
modelFit(model, type=c("twostep", "iter","cue",
"onestep"), itertol=1e7, initW=c("ident", "tsls", "EbyE"),
weights="optimal", itermaxit=100,
efficientWeights=FALSE, theta0=NULL, EbyE=FALSE, ...)
## S4 method for signature 'gelModels'
modelFit(model, gelType=NULL, rhoFct=NULL,
initTheta=c("gmm", "modelTheta0"), theta0=NULL,
lambda0=NULL, vcov=FALSE, ...)
## S4 method for signature 'rgelModels'
modelFit(model, gelType=NULL, rhoFct=NULL,
initTheta=c("gmm", "modelTheta0"), theta0=NULL,
lambda0=NULL, vcov=FALSE, ...)

model 
An object of class 
type 
What GMM methods should we use? for

itertol 
Tolance for the stopping rule in iterative GMM 
initW 
How should be compute the initial coefficient vector in
the first. For single equation GMM, it only makes a difference for
linear models for which the choice is GMM with identity matrix or
twostage least quares. For system of equations, 
weights 
What weighting matrix to use? The choices are

itermaxit 
Maximum iterations for iterative GMM 
efficientWeights 
If 
theta0 
An optional initial vector for 
EbyE 
Should we estimate the system equation by equation? 
... 
Arguments to pass to other methods (mostly the optimization algorithm) 
gelType 
The type of GEL. This argument is only used if we want
to fit the model with a different GEL method. see 
rhoFct 
An alternative objective function for GEL. This argument
is only used if we want to fit the model with a different GEL
method. see 
initTheta 
Method to obtain the starting values for the coefficient vector. By default the GMM estimate with identity matrix is used. The second argument means that the theta0 of the object, if any, should be used. 
lambda0 
Manual starting values for the Lagrange multiplier. By default, it is a vector of zeros. 
vcov 
Should the method computes the covariance matrices of the coefficients and Lagrange multipliers. 
signature(model = "gmmModels")
The main method for all GMMtype models.
signature(model = "gelModels")
The main method for all GELtype models.
signature(model = "rnonlinearGmm")
It makes a difference only if the number of contraints is equal to the
number of coefficients, in which case, the method evalModel
is called at the contrained vector. If not, the next method is called.
signature(model = "rformulaGmm")
It makes a difference only if the number of contraints is equal to the
number of coefficients, in which case, the method evalModel
is called at the contrained vector. If not, the next method is called.
signature(model = "rlinearGmm")
It makes a difference only if the number of contraints is equal to the
number of coefficients, in which case, the method evalModel
is called at the contrained vector. If not, the next method is called.
signature(model = "sysGmmModels")
Method to estimate system of equations using GMM methods.
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  data(simData)
theta < c(beta0=1,beta1=2)
model1 < gmmModel(y~x1, ~z1+z2, data=simData)
## Efficient GMM with HAC vcov and tsls as first step.
res1 < modelFit(model1, init="tsls")
## GMM with identity. Two ways.
res2 < modelFit(model1, type="onestep")
res3 < modelFit(model1, weights=diag(3))
## nonlinear regression with iterative GMM.
g < y~beta0+x1^beta1
h < ~z1+z2
model2 < gmmModel(g, h, c(beta0=1, beta1=2), data=simData)
res4 < modelFit(model2, type="iter")
## GMM for with no endogenous vaiables is
## OLS with Robust standard error
library(lmtest)
model3 < gmmModel(y~x1, ~x1, data=simData, vcov="MDS")
resGmm < modelFit(model3)
resLm < lm(y~x1, simData)
summary(resGmm)
coeftest(resLm, vcov=vcovHC(resLm, "HC0"))
summary(resGmm, df.adj=TRUE)
coeftest(resLm, vcov=vcovHC(resLm, "HC1"))
### All constrained
R < diag(2)
q < c(1,2)
rmodel1 < restModel(model1, R, q)
modelFit(rmodel1)
## Only one constraint
R < matrix(c(0,1), ncol=2)
q < 2
rmodel1 < restModel(model1, R, q)
modelFit(rmodel1)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.