我的代码使用时间固定效果框架中的splm包来估算空间Durbin模型（SDM）。输出显示lambda（空间自相关变量）和控制变量的估计值，但不显示空间滞后控制变量的估计值。因为它是SDM模型，所以我必须得到这个结果。我如何拥有（提取）它。任何提示表示赞赏。
slagerv <- slag(spa.sakp$indexT,listw=ERV) #spatially lagged response
fmerv = (spa.sakp$indexT ~ slagerv + spa.sakp$credT+ spa.sakp$chexT +spa.sakp$uninfT + spa.sakp$gdpT + spa.sakp$chintT+months) #formula
ERVM<- spml(formula = fmerv, data = spa.sakp, index = c('id','months'), listw = ERV, model = "within",
lag = TRUE, effect = "time", spatial.error = "none")#time fixed effect SDM
summary(ERVM)
> summary(ERVM)
Spatial panel fixed effects lag model
Call:
spml(formula = fmied, data = spa.sakp, index = c("id", "months"),
listw = IED, model = "within", effect = "time", lag = TRUE,
spatial.error = "none")
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-0.01159514 -0.00124086 0.00000203 0.00123286 0.00920170
Spatial autoregressive coefficient:
Estimate Std. Error t-value Pr(>|t|)
lambda -1.8126 0.0938 -19.3 <0.0000000000000002 ***
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
slagied -11.8782997 0.0963158 -123.33 < 0.0000000000000002 ***
spa.sakp$credT 0.0047805 0.0015385 3.11 0.0019 **
spa.sakp$chexT 0.0000302 0.0003202 0.09 0.9248
spa.sakp$uninfT 0.0314745 0.0119876 2.63 0.0086 **
spa.sakp$gdpT 0.0215538 0.0326848 0.66 0.5096
spa.sakp$chintT -0.0050297 0.0579541 -0.09 0.9308
months01/01/2001 0.0323093 0.0008751 36.92 < 0.0000000000000002 ***
months01/01/2002 0.0583744 0.0009613 60.73 < 0.0000000000000002 ***
months01/01/2003 0.0728243 0.0010257 71.00 < 0.0000000000000002 ***
[ reached getOption("max.print") -- omitted 201 rows ]
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> r2erv = summary(ERVM)$rsqr
> r2erv
[1] 0.9759
>