boot  R Documentation 
boot: Bootstrap residuals with crosssectional dependence
## S3 method for class 'econet' boot( object, hypothesis = c("lim", "het", "het_l", "het_r", "par", "par_split_with", "par_split_btw", "par_split_with_btw"), group = NULL, niter, weights = FALSE, delta = NULL, na.rm = FALSE, parallel = FALSE, cl, ... )
object 
an object of class 
hypothesis 
string. One of 
group 

niter 
number of required iterations. 
weights 
logical. It is 
delta 
Default is 
na.rm 
logical. Should missing values (including 
parallel 
logical. It is 
cl 
numeric. Number of cores to be used for parallelization. 
... 
additional parameters 
For additional details, see the vignette (doi:10.18637/jss.v102.i08).
Warning: This function is available only when net_dep
is run with estimation == "NLLS"
a numeric vector containing bootstrapped standard errors (see Anselin, 1990). If the procedure is not feasible, it returns a vector of NAs.
Anselin, L., 1990, "Some robust approach to testing and estimation in spatial econometrics", Regional Science and Urban Economics, 20, 141163.
net_dep
# Load data data("db_cosponsor") data("G_alumni_111") db_model_B < db_cosponsor G_model_B < G_cosponsor_111 G_exclusion_restriction < G_alumni_111 are_factors < c("party", "gender", "nchair") db_model_B[are_factors] < lapply(db_model_B[are_factors], factor) # Specify formula f_model_B < formula("les ~gender + party + nchair") # Specify starting values starting < c(alpha = 0.23952, beta_gender1 = 0.22024, beta_party1 = 0.42947, beta_nchair1 = 3.09615, phi = 0.40038, unobservables = 0.07714) # object Linearinmeans model lim_model_B < net_dep(formula = f_model_B, data = db_model_B, G = G_model_B, model = "model_B", estimation = "NLLS", hypothesis = "lim", endogeneity = TRUE, correction = "heckman", first_step = "standard", exclusion_restriction = G_exclusion_restriction, start.val = starting) # Bootstrap # Warning: this may take a very long time to run. # Decrease the number of iterations to reduce runtime. # If you run econet on a Windows platform, you can try to set the # argument parallel = TRUE. However note that this option is still # in its beta version. boot_lim_estimate < boot(object = lim_model_B, hypothesis = "lim", group = NULL, niter = 10, weights = FALSE) boot_lim_estimate
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