spseml: Spatial SUR - Lag and Error
In spse: Spatial Simultaneous Equations and Spatial Sur Model

Description Usage Arguments Details Value Author(s) References See Also Examples

Maximum likelihood estimation of symultaneous systems of spatial SUR model.

1 2	spseml(formula, data=list(),panel=TRUE,index=NULL,w,method="eigen", quiet=NULL, model = c("lag","error"), zero.policy=NULL, interval=NULL, tol.solve=1.0e-10, trs=NULL, control=list(), initval=NULL)

`formula`	a list of objects of class `formula`
`data`	an object of class `data.frame` or `pdata.frame`. An optional data frame containing the variables in the model. When the obect is a data.frame, the first two columns may contain the indexes. See index
`panel`	logical. When TRUE, the data frame is a panel data set with cross-sectional and time observations
`index`	if not NULL (default), a character vector to identify the indexes among the columns of the `data.frame`
`w`	an object of class `listw` created for example by `nb2listw` or a `matrix`
`method`	`spatialsim`
`quiet`	default `NULL`
`model`	`lag` or `error` If `lag`, a spatial lag of the dependent variable is added in each equation. If `error` a spatially autocorrelated error term is assumed in each equation.
`zero.policy`	See `lagsarlm` for details
`interval`	search interval for spatial parameters
`tol.solve`	tolerance
`trs`	default NULL
`control`	A list of control parameters for the optimization
`initval`	the initial values of the spatial parameters

The function can be specified with any number of equations. The number of equations is determined through the formula object. The data can also be specified as a panel data frame. The logical argument PANEL should then be set to TRUE.

An object of class "splm".

`coefficients`	ML coefficients of the model parameters (for all equations)
`vcov`	the variance covariance matrix of the estimated coefficients
`type`	'spsegm'
`model.data`	the matrix of the data used (responses in each equation are reported first, then the explanatory variables)
`model`	`lag` or `error`
`N`	the number of cross-sectional observations
`Eq`	the number of equations in the system
`K`	the total number of explanatory variables in the system
`call`	the call used to create the object
`terms`	the `terms` object generated from `formula` and some optional arguments
`Xnames`	the names of the variables in the matrix of explanatory variables
`Ynames`	the names of the responses
`spec`	the number of explanatory variables in each equation
`lags`	a logical list
`errors`	a logical list
`endogenous`	a logical list
`rho`	the estimated spatial autocorrelation coefficients
`method`	"GM"

Gianfranco Pirasgpiras@mac.com

Anselin, L. (1988) Spatial Econometrics: Methods and Models, Kluwer Academic Publisher.

spsegm

data(Produc, package = "Ecdat")
data(usaww)
Produc <- Produc[Produc$year<1973, ]
eq1 <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp 
eq2 <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp 
eq3 <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp 
formula<-list(tp1 = eq1, tp2 = eq2, tp3=eq3)
listw<-mat2listw(usaww)
sur.error<-spseml(formula, data = Produc, w = listw, model = "error", method = "eigen", quiet = TRUE)
summary(sur.error)
sur.lag<-spseml(formula, data = Produc, w = listw, model = "lag", method = "eigen", quiet = FALSE)
summary(sur.lag)