Fuller | R Documentation |
Fuller
computes the Fuller-k (Fuller 1977) estimate for the ivmodel
object.
Fuller(ivmodel,
beta0 = 0, alpha = 0.05, b = 1,
manyweakSE = FALSE,heteroSE = FALSE,clusterID=NULL)
ivmodel |
|
beta0 |
Null value |
alpha |
The significance level for hypothesis testing. Default is 0.05. |
b |
Positive constant |
manyweakSE |
Should many weak instrument (and heteroscedastic-robust) asymptotics in Hansen, Hausman and Newey (2008) be used to compute standard errors? |
heteroSE |
Should heteroscedastic-robust standard errors be used? Default is FALSE. |
clusterID |
If cluster-robust standard errors are desired, provide a vector of length that's identical to the sample size. For example, if n = 6 and clusterID = c(1,1,1,2,2,2), there would be two clusters where the first cluster is formed by the first three observations and the second cluster is formed by the last three observations. clusterID can be numeric, character, or factor. |
Fuller
computes the Fuller-k estimate for the instrumental variables model in ivmodel
, specifically for the parameter beta
. The computation uses KClass
with the value of k = k_{LIML} - b/(n - L - p)
. It generates a point estimate, a standard error associated with the point estimate, a test statistic and a p value under the null hypothesis H_0: \beta = \beta_0
in ivmodel
along with a 1-\alpha
confidence interval.
Fuller
returns a list containing the following components
k |
The k value used when computing the Fuller estimate with the k-Class estimator. |
point.est |
Point estimate of |
std.err |
Standard error of the estimate. |
test.stat |
The value of the test statistic for testing the null hypothesis |
p.value |
The p value of the test under the null hypothesis |
ci |
A matrix of one row by two columns specifying the confidence interval associated with the Fuller estimator. |
Yang Jiang, Hyunseung Kang, Dylan Small
Fuller, W. (1977). Some properties of a modification of the limited information estimator. Econometrica, 45, 939-953.
See also ivmodel
for details on the instrumental variables model. See also KClass
for more information about the k-Class estimator.
data(card.data)
Y=card.data[,"lwage"]
D=card.data[,"educ"]
Z=card.data[,c("nearc4","nearc2")]
Xname=c("exper", "expersq", "black", "south", "smsa", "reg661",
"reg662", "reg663", "reg664", "reg665", "reg666", "reg667",
"reg668", "smsa66")
X=card.data[,Xname]
card.model2IV = ivmodel(Y=Y,D=D,Z=Z,X=X)
Fuller(card.model2IV,alpha=0.01)
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