lsem {spmoran}

Low rank spatial error model (LSEM) estimation

Description

This function estimates the low rank spatial error model.

Usage

lsem( y, x, weig, method = "reml" )

Arguments

y

Vector of explained variables (N x 1)

x

Matrix of explanatory variables (N x K)

weig

eigenvectors and eigenvalues of a spatial weight matrix. Output from weigen

method

Estimation method. Restricted maximum likelihood method ("reml") and maximum likelihood method ("ml") are available. Default is "reml"

Value

b

Matrix with columns for the estimated coefficients on x, their standard errors, t-values, and p-values (K x 4)

s

Vector of estimated variance parameters (2 x 1). The first and the second elements denote the estimated rho parameter (sp_lambda) quantfying the scale of spatial dependent process, and the standard error of the process (sp_SE), respectively.

e

Vector whose elements are residual standard error (resid_SE), adjusted conditional R2 (adjR2(cond)), restricted log-likelihood (rlogLik), Akaike information criterion (AIC), and Bayesian information criterion (BIC). When method = "ml", restricted log-likelihood (rlogLik) is replaced with log-likelihood (logLik)

r

Vector of estimated random coefficients on the spatial eigenvectors (L x 1)

pred

Vector of predicted values (N x 1)

resid

Vector of residuals (N x 1)

other

List of other outputs, which are internally used

Author(s)

Daisuke Murakami

References

Murakami, D., Seya, H. and Griffith, D.A. (2018) Low rank spatial econometric models. Arxiv.

See Also

meigen, meigen_f

Examples

require(spdep)
data(boston)
y	<- boston.c[, "CMEDV" ]
x	<- boston.c[,c("CRIM","ZN","INDUS", "CHAS", "NOX","RM", "AGE",
                       "DIS" ,"RAD", "TAX", "PTRATIO", "B", "LSTAT")]
coords<- boston.c[,c("LON", "LAT")]
weig 	<- weigen( coords )
res	  <- lsem(y=y,x=x,weig=weig)
res