stsls {spdep} | R Documentation

## Generalized spatial two stage least squares

### Description

The function fits a spatial lag model by two stage least squares, with the option of adjusting the results for heteroskedasticity.

### Usage

    stsls(formula, data = list(), listw, zero.policy = FALSE,
     na.action = na.fail, robust = FALSE,legacy=FALSE)

### Arguments

`formula` | a symbolic description of the model to be fit. The details of model specification are given for `lm()`
`data` | an optional data frame containing the variables in the model. By default the variables are taken from the environment which the function is called.
`listw` | a `listw` object created for example by `nb2listw`
`zero.policy` | if TRUE assign zero to the lagged value of zones without neighbours, if FALSE (default) assign NA - causing `lagsarlm()` to terminate with an error
`na.action` | a function (default `na.fail`), can also be `na.omit` or `na.exclude` with consequences for residuals and fitted values - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to `nb2listw` may be subsetted.
`robust` | default FALSE, if TRUE, apply a heteroskedasticity correction to the coefficients covariances
`legacy` | the argument chooses between two implementations of the robustness correction: default FALSE - use the estimate of Omega only in the White consistent estimator of the variance-covariance matrix, if TRUE, use the original implementation which runs a GLS using the estimate of Omega, and yields different coefficient estimates as well

### Details

The fitting implementation fits a spatial lag model:

y = rho W y + X beta + e

by using spatially lagged X variables as instruments for the spatially lagged dependent variable.

### Value

an object of class "stsls" containing:

`coefficients` | coefficient estimates
`var` | coefficient covariance matrix
`sse` | sum of squared errors
`residuals` | model residuals
`df` | degrees of freedom

### Author(s)

Luc Anselin, Gianfranco Piras and Roger Bivand

### References

Kelejian, H.H. and I.R. Prucha (1998). A generalized spatial two stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. Journal of Real Estate Finance and Economics 17, 99-121.

### See Also

`lagsarlm`

### Examples

    data(oldcol)
    COL.lag.eig <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, nb2listw(COL.nb))
    summary(COL.lag.eig, correlation=TRUE)
    COL.lag.stsls <- stsls(CRIME ~ INC + HOVAL, data=COL.OLD, nb2listw(COL.nb))
    summary(COL.lag.stsls, correlation=TRUE)
    COL.lag.stslsR <- stsls(CRIME ~ INC + HOVAL, data=COL.OLD, nb2listw(COL.nb),
    robust=TRUE)
    summary(COL.lag.stslsR, correlation=TRUE)
    data(boston)
    gp2a <- stsls(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) +
      AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT),
     data=boston.c, nb2listw(boston.soi))
    summary(gp2a)

[Package spdep version 0.4-56 Index]