| Title: | Robust Maximum Likelihood Estimation for Spatial Error Model |
|---|---|
| Description: | This package provides robust maximum likelihood estimation for spatial error model. |
| Authors: | Vural Yildirim [aut,cre], Yeliz Mert Kantar [aut,ths] |
| Maintainer: | Vural Yildirim <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-11-07 02:39:44 UTC |
| Source: | https://github.com/wyilldirim/spatialromle |
Initial coefficients of individual pension system data were obtained by MLE.
IPS_coefsIPS_coefs
A list with 10 values, which are:
intercept
labor rate
unemployment rate
sex ratio
urbanization rate
deposit rate
illiteracy rate
human development index
spatial autocorrelation parameter
variance
This is individual pension system data of Turkey for analysing spatial error model.
IPS_dataIPS_data
A list with 10 variables, which are:
provinces ID
provinces names
participant rate of individual pension system
labor rate
unemployment rate
sex ratio
urbanization rate
deposit rate
illiteracy rate
human development index
This package provides robust maximum likelihood estimation for spatial error model.
RoMLE.error( initial.beta, initial.s2, initial.lambda, W, y, x, phi.function, converge.v, iter, print.values )RoMLE.error( initial.beta, initial.s2, initial.lambda, W, y, x, phi.function, converge.v, iter, print.values )
initial.beta |
initial value of coefficients |
initial.s2 |
initial value of varaince |
initial.lambda |
initial value of autocorrelation parameters |
W |
a symmetric weight matrix |
y |
dependent variable |
x |
independent variables |
phi.function |
a robust m-estimator function, should be set as 1 for Cauchy, 2 for Welsch, 3 for Insha and 4 for Logistic |
converge.v |
converge value for fisher scoring algorithm, can be set as 1e-04 |
iter |
iteration number for fisher scoring algorithm, set by users (e.g. 100) |
print.values |
printing estimated values for each step until converge, should be set TRUE or FALSE |
coefficients, lambda, s2, Phi
Yildirim, V. and Kantar, Y.M. (2020). Robust estimation of spatial error model. in Journal of Statistical Computation and Simulation https://doi.org/10.1080/00949655.2020.1740223
Yildirim, V., Mert Kantar, Y. (2019). Spatial Statistical Analysis of Participants in The Individual Pension System of Turkey. Eskisehir Teknik Universitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler, 7(2), 184-194 https://doi.org/10.20290/estubtdb.518706
#spdep library can be used to create a weight matrix from listw #require(spdep) #W <- as(listw, "CsparseMatrix") #example 1 data(TRQWM) data(unemployment_data) data(unemployment_coefs) y <- unemployment_data$unemployment x <- unemployment_data$urbanization #initial values was taken from MLE initial.beta <- unemployment_coefs[1:2,2] initial.lambda <- unemployment_coefs[3,2] initial.s2 <- unemployment_coefs[4,2] RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x, phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE) #example 2 data(TRQWM) data(IPS_data) data(IPS_coefs) y <- IPS_data[,3] x <- IPS_data[,4:10] #initial values was taken from MLE initial.beta <- IPS_coefs[1:8,2] initial.lambda <- IPS_coefs[9,2] initial.s2 <- IPS_coefs[10,2] RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x, phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE)#spdep library can be used to create a weight matrix from listw #require(spdep) #W <- as(listw, "CsparseMatrix") #example 1 data(TRQWM) data(unemployment_data) data(unemployment_coefs) y <- unemployment_data$unemployment x <- unemployment_data$urbanization #initial values was taken from MLE initial.beta <- unemployment_coefs[1:2,2] initial.lambda <- unemployment_coefs[3,2] initial.s2 <- unemployment_coefs[4,2] RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x, phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE) #example 2 data(TRQWM) data(IPS_data) data(IPS_coefs) y <- IPS_data[,3] x <- IPS_data[,4:10] #initial values was taken from MLE initial.beta <- IPS_coefs[1:8,2] initial.lambda <- IPS_coefs[9,2] initial.s2 <- IPS_coefs[10,2] RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x, phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE)
Robust Maximum Likelihood Estimation for Spatial Error Model.
Vural Yildirim [email protected]
Yeliz Mert Kantar
Yildirim, V. and Kantar, Y.M. (2020). Robust estimation of spatial error model. in Journal of Statistical Computation and Simulation. https://doi.org/10.1080/00949655.2020.1740223
This is queen continugity weight matrix of Turkey.
TRQWMTRQWM
A symmetric matrix with 81x81 values,
provinces ID
Initial coefficients of unemployment data were obtained by MLE.
unemployment_coefsunemployment_coefs
A list with 4 values, which are:
intercept
unemployment rate
spatial autocorrelation parameter
variance
This is unemployment data of Turkey for analysing spatial error model.
unemployment_dataunemployment_data
A list with 4 variables, which are:
provinces ID
provinces names
unemployment rate
urbanization rate