boot | R Documentation |
boot: Bootstrap residuals with cross-sectional 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, 141-163.
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 Linear-in-means 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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