gstslshet {sphet} | R Documentation |
GM estimation of a Cliff-Ord type model with Heteroskedastic Innovations
Description
Multi step GM/IV estimation of a linear Cliff and Ord -type of model of the form:
y=\lambda W y + X \beta + u
u=\rho W u + e
with
e ~ N(0,\sigma^2_i)
The model allows for spatial lag in the dependent variable and disturbances. The innovations in the disturbance process are assumed heteroskedastic of an unknown form.
Usage
gstslshet(formula, data = list(), listw, na.action = na.fail,
zero.policy = NULL, initial.value = 0.2, abs.tol = 1e-20,
rel.tol = 1e-10, eps = 1e-5, inverse = T, sarar = T)
Arguments
formula |
a description of the model to be fit |
data |
an object of class data.frame. An optional data frame containing the variables in the model. |
listw |
an object of class |
na.action |
a function which indicates what should happen when the data contains missing values. See lm for details. |
zero.policy |
See |
initial.value |
The initial value for |
abs.tol |
Absolute tolerance. See nlminb for details. |
rel.tol |
Relative tolerance. See nlminb for details. |
eps |
Tolerance level for the approximation. See Details. |
inverse |
|
sarar |
|
Details
The procedure consists of two steps alternating GM and IV estimators. Each step consists of sub-steps.
In step one \delta = [\beta',\lambda]'
is estimated by 2SLS. The 2SLS residuals are first employed
to obtain an initial (consistent but not efficient) GM estimator of \rho
and then a consistent and efficient
estimator (involving the variance-covariance matrix of the limiting distribution of the normalized sample moments).
In step two, the spatial Cochrane-Orcutt transformed model is estimated by 2SLS. This corresponds to a GS2SLS procedure.
The GS2SLS residuals are used to obtain a consistent and efficient GM estimator for \rho
.
The initial value for the optimization in step 1b is taken to be initial.value
. The initial value in step 1c is the
optimal parameter of step 1b. Finally, the initial value for the optimization of step 2b is the optimal parameter of step 1c.
Internally, the object of class listw
is transformed into a Matrix
using the function listw2dgCMatrix.
The expression of the estimated variance covariance matrix of the limiting
distribution of the normalized sample moments based on 2SLS residuals
involves the inversion of I-\rho W'
.
When inverse
is FALSE
, the inverse is calculated using the approximation
I +\rho W' + \rho^2 W'^2 + ...+ \rho^n W'^n
.
The powers considered depend on a condition.
The function will keep adding terms until the absolute value of the sum
of all elements
of the matrix \rho^i W^i
is greater than a fixed \epsilon
(eps
). By default eps
is set to 1e-5.
Value
A list object of class sphet
coefficients |
Generalized Spatial two stage least squares coefficient estimates of |
var |
variance-covariance matrix of the estimated coefficients |
s2 |
GS2SLS residuals variance |
residuals |
GS2SLS residuals |
yhat |
difference between GS2SLS residuals and response variable |
call |
the call used to create this object |
model |
the model matrix of data |
method |
|
W |
Wald test for both |
Author(s)
Gianfranco Piras gpiras@mac.com
References
Arraiz, I. and Drukker, M.D. and Kelejian, H.H. and Prucha, I.R. (2007) A spatial Cliff-Ord-type Model with Heteroskedastic Innovations: Small and Large Sample Results, Department of Economics, University of Maryland'
Kelejian, H.H. and Prucha, I.R. (2007) Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances, Journal of Econometrics, forthcoming.
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.
See Also
Examples
data(columbus, package = "spdep")
listw <- spdep::nb2listw(col.gal.nb)
res <- gstslshet(CRIME ~ HOVAL + INC, data = columbus, listw = listw)
summary(res)