Spatial Panel Model with Random Effects by Maximum Likelihood
Maximum likelihood (ML) estimation of spatial panel models with random effects and serial error correlation.
spreml(formula, data, index = NULL, w, w2=w, lag = FALSE,
errors = c("semsrre", "semsr", "srre", "semre",
"re", "sr", "sem","ols", "sem2srre", "sem2re"),
pvar = FALSE, hess = FALSE, quiet = TRUE,
initval = c("zeros", "estimate"),
x.tol = 1.5e-18, rel.tol = 1e-15, ...)formula |
a symbolic description of the model to be estimated |
data |
an object of class |
index |
if not NULL (default), a character vector to identify the indexes among the columns of the |
w |
an object of class |
w2 |
an object of class |
lag |
default= |
errors |
Specifies the error covariance structure. See details. |
pvar |
legacy parameter here only for compatibility. |
hess |
default= |
quiet |
default= |
initval |
one of |
x.tol |
control parameter for tolerance. See |
rel.tol |
control parameter for relative tolerance. See |
... |
additional arguments to pass over to other functions, e.g. |
Second-level wrapper for estimation of random effects models
with serial and spatial correlation. The specifications without serial
correlation (no "sr" in errors) can be called through
spml, the extended ones only through spreml.
The models are estimated by two-step Maximum Likelihood.
Abbreviations in errors correspond to: "sem"
Anselin-Baltagi type spatial autoregressive error: if
present, random effects are not spatially correlated; "sem2"
Kapoor, Kelejian and Prucha-type spatial autoregressive error model
with spatially correlated random effects; "sr" serially
correlated remainder errors; "re" random effects; "ols"
spherical errors (usually combined with lag=T).
The optimization method can be passed on as optional
parameter. Default is "nlminb"; all constrained optimization
methods from maxLik are allowed ("BFGS", "NM", "SANN")
but the latter two are still experimental.
An object of class "splm".
coefficients |
coefficients estimate of the model parameters |
arcoef |
the coefficient for the spatial lag on |
errcomp |
the estimates of the error variance components |
vcov |
the asymptotic variance covariance matrix of the estimated coefficients |
vcov.arcoef |
the asymptotic variance of the estimated spatial lag parameter |
vcov.errcomp |
the asymptotic variance covariance matrix of the estimated error covariance parameters |
type |
'random effects ML' |
residuals |
the model residuals |
fitted.values |
the fitted values, calculated as \hat{y}=X \hat{β} |
sigma2 |
GLS residuals variance |
model |
the matrix of the data used |
call |
the call used to create the object |
logLik |
the value of the log likelihood function at the optimum |
errors |
the value of the |
Giovanni Millo
Millo, G. (2014) Maximum likelihood estimation of spatially and serially correlated panels with random effects. Computational Statistics and Data Analysis, 71, 914–933.
spml
data(Produc, package = "plm")
data(usaww)
fm <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp
## random effects panel with spatial lag and serial error correlation
## optimization method set to "BFGS"
sarsrmod <- spreml(fm, data = Produc, w = usaww, errors="sr", lag=TRUE,
method="BFGS")
summary(sarsrmod)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.