lr_betas {spsur} | R Documentation |
Likelihood ratio for testing homogeneity constraints on beta coefficients of the SUR equations.
Description
Function lr_betas
obtains a Likelihood Ratio test, LR in what follows,
with the purpose of testing if some of the \beta
coefficients in the G equations of the
SUR model are equal. This function has a straightforward application, especially when G=1
,
to the case of testing for the existence of structural breaks in the \beta
parameters.
The function can test for the homogeneity of only one coefficient, of a few of them or even the homogeneity of all the slope terms. The testing procedure implies, first, the estimation of both a constrained and a unconstrained model and, second, the comparison of the log-likelihoods to compute the LR statistics.
@usage lr_betas (obj, R, b)
Usage
lr_betas(obj, R, b)
Arguments
obj |
|
R |
A row vector of order |
b |
A column vector of order (rx1) with the values of the
linear restrictions on the |
Value
Object of htest
including the LR
statistic, the corresponding p-value, the degrees of
freedom and the values of the sample estimates.
Author(s)
Fernando Lopez | fernando.lopez@upct.es |
Roman Minguez | roman.minguez@uclm.es |
Jesus Mur | jmur@unizar.es |
References
Mur, J., Lopez, F., and Herrera, M. (2010). Testing for spatial effects in seemingly unrelated regressions. Spatial Economic Analysis, 5(4), 399-440. <doi:10.1080/17421772.2010.516443>
Minguez, R., Lopez, F.A. and Mur, J. (2022). spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models. Journal of Statistical Software, 104(11), 1–43. <doi:10.18637/jss.v104.i11>
See Also
spsurml
, spsurtime
, wald_betas
Examples
## VIP: The output of the whole set of the examples can be examined
## by executing demo(demo_lr_betas, package="spsur")
#' #################################################
######## CROSS SECTION DATA (G>1; Tm=1) ########
#################################################
#### Example 1: Spatial Phillips-Curve. Anselin (1988, p. 203)
rm(list = ls()) # Clean memory
data(spc)
lwspc <- spdep::mat2listw(Wspc, style = "W")
Tformula <- WAGE83 | WAGE81 ~ UN83 + NMR83 + SMSA | UN80 + NMR80 + SMSA
### H0: equal beta for SMSA in both equations.
R <- matrix(c(0,0,0,1,0,0,0,-1), nrow=1)
b <- matrix(0, ncol=1)
spcsur.slm <- spsurml(formula = Tformula, data = spc,
type = "slm", listw = lwspc)
summary(spcsur.slm)
lr_betas(spcsur.slm, R = R, b = b)
### Estimate restricted SUR-SLM model
spcsur.slmr <- spsurml(formula = Tformula, data = spc,
type = "slm", listw = lwspc,
R = R, b = b)
summary(spcsur.slmr)