spml: Spatial Panel Model by Maximum Likelihood

In splm: Econometric Models for Spatial Panel Data

Spatial Panel Model by Maximum Likelihood

Description

Maximum likelihood (ML) estimation of spatial panel models, possibly with fixed or random effects.

Usage

spml(formula, data, index=NULL, listw, listw2=listw, na.action,
                 model=c("within","random","pooling"),
                 effect=c("individual","time","twoways"),
                 lag=FALSE, spatial.error=c("b","kkp","none"),
                 ...)
## S3 method for class 'splm_ML'
impacts(obj, listw = NULL,
                         time = NULL, ...,
                         tr = NULL, R = 200,
                         type = "mult",
                         empirical = FALSE, Q = NULL)
## S3 method for class 'splm_GM'
impacts(obj, ..., tr=NULL, 
                            R=NULL, listw=NULL,
                            type = "mult",
                            time = NULL,
                            evalues=NULL, tol=1e-6, 
                            empirical=FALSE, Q=NULL, 
                            KPformula = FALSE, prt = TRUE)

Arguments

formula	a symbolic description of the model to be estimated
data	an object of class data.frame or pdata.frame. A data frame containing the variables in the model. When the object is a data.frame, the first two columns shall contain the indexes, unless otherwise specified. See index
index	if not NULL (default), a character vector to identify the indexes among the columns of the data.frame
listw	an object of class listw or a matrix. It represents the spatial weights to be used in estimation.
listw2	an object of class listw or a matrix. Second of set spatial weights for estimation, if different from the first (e.g., in a 'sarar' model).
na.action	see spdep for more details.
model	one of c("within", "random", "pooling").
effect	one of c("individual","time","twoways"); the effects introduced in the model.
lag	default=FALSE. If TRUE, a spatial lag of the dependent variable is added.
spatial.error	one of c("b","kkp","none"). The type of spatial error in the specification, if any. See details.
...	additional argument to pass over to other functions
obj	fitted model object
time	??time??
tr	A vector of traces of powers of the spatial weights matrix created using 'trW', for approximate impact measures
R	If given, simulations are used to compute distributions for the impact measures, returned as 'mcmc' objects
type	Either "mult" (default) for powering a sparse matrix (with moderate or larger N, the matrix becomes dense, and may lead to swapping), or "MC" for Monte Carlo simulation of the traces (the first two simulated traces are replaced by their analytical equivalents), or "moments" to use the looping space saving algorithm proposed by Smirnov and Anselin (2009) - for "moments", 'W' must be symmetric, for row-standardised weights through a similarity transformation
empirical	Argument passed to 'mvrnorm' (default FALSE)
Q	default NULL, else an integer number of cumulative power series impacts to calculate if 'tr' is given
evalues	vector of eigenvalues of spatial weights matrix for impacts calculations
tol	Argument passed to 'mvrnorm'
KPformula	not yet implemented
prt	not yet implemented

Details

The models are estimated by two-step Maximum Likelihood. Further optional parameters to be passed on to the estimator may be: pvar: if TRUE the pvar function is called hess: if TRUE use numerical Hessian instead of GLS for the standard errors of the estimates quiet: if FALSE report function and parameters values during optimization initval: one of c("zeros", "estimate"), the initial values for the parameters. If "zeros" a vector of zeros is used. if "estimate" the initial values are retreived from the estimation of the nested specifications. Alternatively, a numeric vector can be specified. x.tol: Tolerance. See nlminb for details. rel.tol: Relative tolerance. See nlminb for details.

Value

An object of class "splm".

coefficients	coefficients estimate of the model parameters
arcoef	the coefficient for the spatial lag on y
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{\beta}
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 errors argument

Author(s)

Giovanni Millo

References

Baltagi, B.H., Song, S.H., Jung B. and Koh, W. (2007) Testing panel data regression models with spatial and serial error correlation. Journal of Econometrics, 140, 5-51.

Millo, G., Piras, G. (2012) splm: Spatial Panel Data Models in R. Journal of Statistical Software, 47(1), 1–38. URL http://www.jstatsoft.org/v47/i01/.

See Also

spgm

Examples

data(Produc, package = "plm")
data(usaww)
fm <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp
## the two standard specifications (SEM and SAR) one with FE
## and the other with RE:
## fixed effects panel with spatial errors
fespaterr <- spml(fm, data = Produc, listw = spdep::mat2listw(usaww),
                   model="within", spatial.error="b", Hess = FALSE)
summary(fespaterr)
## random effects panel with spatial lag
respatlag <- spml(fm, data = Produc, listw = spdep::mat2listw(usaww),
                   model="random", spatial.error="none", lag=TRUE)
summary(respatlag)
## calculate impact measures
#impac1 <- impacts.splm(respatlag, listw = spdep::mat2listw(usaww, #style = "W"), time = 17)
#summary(impac1, zstats=TRUE, short=TRUE)

splm documentation built on Dec. 12, 2023, 3:03 a.m.