It computes the optimal bandwidth for the HAC estimation of the covariance matrix of the moment conditions. The bandwidth was shown by Wilhelm (2005) to be the one that minimizes the MSE of the GMM estimator.
1 2 3 
x 
An object of class 
order.by 
Either a vector 'z' or a formula with a single explanatory variable like '~ z'. The observations in the model are ordered by the size of 'z'. If set to 'NULL' (the default) the observations are assumed to be ordered (e.g., a time series). 
kernel 
type of kernel used to compute the covariance matrix of the vector of sample moment conditions (see 
approx 
A character specifying the approximation method if the bandwidth has to be chosen by 
weights 
numeric. A vector of weights used for weighting the estimated coefficients of the approximation model (as specified by 'approx'). By default all weights are 1 except that for the intercept term (if there is more than one variable) 
prewhite 
logical or integer. Should the estimating functions be prewhitened? If 
ar.method 
character. The 
data 
an optional data frame containing the variables in the 'order.by' model. 
The function 'bwWilhelm' returns the optimal bandwidth.
The function was written by Daniel Wilhelm and is based on bwAndrews.
Wilhelm, D. (2015), Optimal Bandwidth Selection for Robust Generalized Method of Moments Estimation. Econometric Theory, 31, 1054–1077
Zeileis A (2006), Objectoriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1–16. URL http://www.jstatsoft.org/v16/i09/.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  data(Finance)
f1 < Finance[1:300, "rm"]
f2 < Finance[1:300, "hml"]
f3 < Finance[1:300, "smb"]
y < Finance[1:300,"WMK"]
## Silly example just to make it overidentified
###############################################
res < gmm(y ~ f1, ~ f1 + f2 + f3)
summary(res)
## Set the bandwidth using the second step estimate
################################################
bw < bwWilhelm(res)
res2 < update(res, bw=bw)
summary(res2)
## Set the bandwidth using the firststep estimate as for bwAndrews
###################################################################
res3 < gmm(y ~ f1, ~ f1 + f2 + f3, bw=bwWilhelm)
summary(res3)

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