MinimumDistance: Estimation and Inference for Minimum-Distance Estimator

In zhenxie23/SpReg: a Toolkit of Spatial Econometrics with Misaligned Data

Description

This function implements the esmation and large sample inference for Minimum-Distance Estimator.

Usage

MinimumDistance(DatR, VarR, DatY, VarY, variogram.model,
                       MD.starting.value, cutoff.R, cutoff.YR,
                       start.value.method = 2, projected = FALSE)

## S3 method for class 'MinimumDistance'
summary(object, ...)

Arguments

DatR	explanatory variable R, a spatial object, see coordintes()
VarR	name of variable R
DatY	outcome variable Y, a spatial object, see coordintes()
VarY	name of variable Y
variogram.model	variogram model type, e.g. "Exp", "Sph", "Gau", "Mat"
MD.starting.value	the starting point of parameters
cutoff.R	cutoff for sample variogram of variable R
cutoff.YR	cutoff for sample cross variogram of variable R and Y
start.value.method	fitting method, see fit.variogram()
projected	logical; if FALSE, data are assumed to be unprojected, meaning decimal longitude/latitude. For projected data, Euclidian distances are computed, for unprojected great circle distances(km) are computed.
object	class MinimumDistance objects.

Value

num.obs	the number of observations
vario.par.point.est	point estimates for variogram parameters(psill, range)
vario.par.var.mat	estimated variance-covariance matrix for variogram parameters(psill, range)
md.point.est	point estimates for Min-Dist estimator
md.var.mat	estimated aymptotic variance for Min-Dist estimator

Methods (by generic)

• summary: summary method for class "MinimumDistance".

See Also

sp, gstat

zhenxie23/SpReg documentation built on March 26, 2021, 3:09 a.m.