R: Spatial two stages least square with HAC standard errors
stslshac
R Documentation
Spatial two stages least square with HAC standard errors
Description
Non-parametric heteroskedasticity and autocorrelation
consistent (HAC) estimator of the variance-covariance (VC) for a vector of sample moments within a
spatial context. The disturbance vector is generated as follows:
an object of class data.frame. An optional data frame containing the variables
in the model.
listw
an object of class listw created for example by nb2listw
distance
an object of class distance created for example by read.gwt2dist
The object contains the specification of the distance measure
to be employed in the estimation of the VC matrix. See Details.
type
One of c("Epanechnikov","Triangular","Bisquare","Parzen", "QS","TH").
The type of Kernel to be used. See Details.
na.action
a function which indicates what should happen when the data contains missing values.
See lm for details.
zero.policy
See lagsarlm for details
bandwidth
"variable" (default) - or numeric when a fixed bandwidth is specified by the user.
HAC
if FALSE traditional standard errors are provided.
W2X
default TRUE. if FALSE only WX are used as instruments in the spatial two stage least squares.
Details
The default sets the bandwith for each observation to the maximum distance for that observation (i.e.
the max of each element of the list of distances).
Six different kernel functions are implemented:
'Epanechnikov': K(z) = 1-z^2
'Triangular': K(z) = 1-z
'Bisquare': K(z) = (1-z^2)^2
'Parzen': K(z) = 1-6z^2+6 |z|^3 if z ≤q 0.5 and
K(z) = 2(1-|z|)^3 if 0.5 < z ≤q 1
If the kernel type is not one of the six implemented, the function will terminate with an error message.
The spatial two stage least square estimator is based on the matrix of instruments H=[X,WX,W^2X^2].
Value
A list object of class sphet
coefficients
Spatial two stage least squares coefficient estimates
vcmat
variance-covariance matrix of the estimated coefficients
s2
S2sls residulas variance
residuals
S2sls residuals
yhat
difference between residuals and response variable
Kelejian, H.H. and Prucha, I.R. (2007)
HAC estimation in a spatial framework,
Journal of Econometrics, 140, pages 131–154.
Kelejian, H.H. and Prucha, I.R. (1999)
A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model,
International Economic Review, 40, pages 509–533.
Kelejian, H.H. and Prucha, I.R. (1998)
A Generalized Spatial Two Stage Least Square Procedure for Estimating a Spatial Autoregressive
Model with Autoregressive Disturbances,
Journal of Real Estate Finance and Economics, 17, pages 99–121.