splm 包：spgm() 函数中的仪器似乎不起作用
splm package: Instruments in spgm() function don't seem to work
基本上，来自 splm 包的 spgm() 函数内部的检测似乎不起作用，因为它显示其与基本模型的估计结果没有差异（即，相同的公式但没有仪器).
我试过以下方法：
- 将参数
method 更改为所有可能性（w2sls、b2sls、g2sls 和 ec2sls）
- 使用时不变变量作为工具（在 Produc data.frame 中，这将是变量 'region'），而不是区域不变变量，如可重现示例所示之后。
- 在 SAR、SEM 和 SAC 规范之间更改（即
lag 和 spatial.error 参数的每种可能组合，它们是布尔值）
我似乎无法找到它不起作用的原因。这是一个使用 plm 包中的面板数据 data.frame 的可重现示例：
library(splm)
library(plm)
sessionInfo()
## R version 4.0.5 (2021-03-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Galician_Spain.1252 LC_CTYPE=Galician_Spain.1252 LC_MONETARY=Galician_Spain.1252 LC_NUMERIC=C
## [5] LC_TIME=Galician_Spain.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.31 plm_2.4-1 splm_1.5-2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 bdsmatrix_1.3-4 lattice_0.20-41 deldir_0.2-10 class_7.3-18 zoo_1.8-8 gtools_3.8.2
## [8] assertthat_0.2.1 digest_0.6.27 lmtest_0.9-38 utf8_1.1.4 R6_2.5.0 evaluate_0.14 coda_0.19-4
## [15] e1071_1.7-4 spam_2.6-0 highr_0.8 pillar_1.5.1 Rdpack_2.1.1 miscTools_0.6-26 rlang_0.4.10
## [22] spdep_1.1-5 gdata_2.18.0 raster_3.4-5 gmodels_2.18.1 Matrix_1.3-2 splines_4.0.5 stringr_1.4.0
## [29] xfun_0.22 compiler_4.0.5 pkgconfig_2.0.3 maxLik_1.4-6 tidyselect_1.1.0 tibble_3.0.6 expm_0.999-6
## [36] codetools_0.2-18 fansi_0.4.2 crayon_1.4.1 dplyr_1.0.5 sf_0.9-7 MASS_7.3-53.1 rbibutils_2.0
## [43] spatialreg_1.1-5 grid_4.0.5 nlme_3.1-152 spData_0.3.8 lifecycle_1.0.0 DBI_1.1.1 magrittr_2.0.1
## [50] units_0.7-0 ibdreg_0.3.1 KernSmooth_2.23-18 stringi_1.5.3 pryr_0.1.4 LearnBayes_2.15.1 sp_1.4-5
## [57] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.6 boot_1.3-27 sandwich_3.0-0 Formula_1.2-4 tools_4.0.5
## [64] forcats_0.5.1 glue_1.4.2 markdown_1.1 purrr_0.3.4 parallel_4.0.5 classInt_0.4-3 dotCall64_1.0-1
data(Produc)
data(usaww)
head(Produc[c("gsp", "pcap", "pc", "hwy")])
## gsp pcap pc hwy
## 1 28418 15032.67 35793.80 7325.80
## 2 29375 15501.94 37299.91 7525.94
## 3 31303 15972.41 38670.30 7765.42
## 4 33430 16406.26 40084.01 7907.66
## 5 33749 16762.67 42057.31 8025.52
## 6 33604 17316.26 43971.71 8158.23
# Baseline model. No instruments
GM <- spgm(log(gsp) ~ log(pcap) + log(pc), data=Produc,
lag = TRUE,
spatial.error = FALSE,
method = "w2sls",
listw = usaww)
## Warning in if (model == "fixed" & !isTRUE(attr(terms(formula), "intercept"))) formula <- as.formula(paste(attr(terms(formula), : the condition has
## length > 1 and only the first element will be used
summary(GM)
##
## Call:spgm(formula = log(gsp) ~ log(pcap) + log(pc), data = Produc, listw = usaww, lag = TRUE, spatial.error = FALSE, method = "w2sls")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2054157 -0.0290282 0.0017208 0.0260133 0.2340702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## lambda 0.367638 0.043093 8.5313 < 2e-16
## log(pcap) 0.087213 0.034721 2.5118 0.01201
## log(pc) 0.498388 0.037026 13.4603 < 2e-16
##
## Residual variance (sigma squared): 0.0023562, (sigma: 0.048541)
# Model with instruments (log(hwy) is being used to instrument log(pcap))
GM_instru <- spgm(log(gsp) ~ log(pcap) + log(pc), data=Produc,
lag = TRUE,
spatial.error = FALSE,
method = "w2sls",
instruments = ~log(hwy) + log(pc),
listw = usaww)
## Warning in if (model == "fixed" & !isTRUE(attr(terms(formula), "intercept"))) formula <- as.formula(paste(attr(terms(formula), : the condition has
## length > 1 and only the first element will be used
summary(GM_instru)
##
## Call:spgm(formula = log(gsp) ~ log(pcap) + log(pc), data = Produc, listw = usaww, lag = TRUE, spatial.error = FALSE, instruments = ~log(hwy) +
## log(pc), method = "w2sls")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2054157 -0.0290282 0.0017208 0.0260133 0.2340702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## lambda 0.367638 0.043093 8.5313 < 2e-16
## log(pcap) 0.087213 0.034721 2.5118 0.01201
## log(pc) 0.498388 0.037026 13.4603 < 2e-16
##
## Residual variance (sigma squared): 0.0023562, (sigma: 0.048541)
为什么检测不起作用？
因为您没有正确指定模型。
尝试：
GM_instru <- spgm(log(gsp) ~ log(pc), data=Produc,
lag = TRUE,
spatial.error = FALSE, endog = ~log(pcap),
method = "w2sls",
instruments = ~log(hwy),
listw = usaww)
其实内生变量是有自变量的，不应该在公式对象中指定。
基本上，来自 splm 包的 spgm() 函数内部的检测似乎不起作用，因为它显示其与基本模型的估计结果没有差异（即，相同的公式但没有仪器).
我试过以下方法：
- 将参数
method更改为所有可能性（w2sls、b2sls、g2sls和ec2sls） - 使用时不变变量作为工具（在 Produc data.frame 中，这将是变量 'region'），而不是区域不变变量，如可重现示例所示之后。
- 在 SAR、SEM 和 SAC 规范之间更改（即
lag和spatial.error参数的每种可能组合，它们是布尔值）
我似乎无法找到它不起作用的原因。这是一个使用 plm 包中的面板数据 data.frame 的可重现示例：
library(splm)
library(plm)
sessionInfo()
## R version 4.0.5 (2021-03-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Galician_Spain.1252 LC_CTYPE=Galician_Spain.1252 LC_MONETARY=Galician_Spain.1252 LC_NUMERIC=C
## [5] LC_TIME=Galician_Spain.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.31 plm_2.4-1 splm_1.5-2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 bdsmatrix_1.3-4 lattice_0.20-41 deldir_0.2-10 class_7.3-18 zoo_1.8-8 gtools_3.8.2
## [8] assertthat_0.2.1 digest_0.6.27 lmtest_0.9-38 utf8_1.1.4 R6_2.5.0 evaluate_0.14 coda_0.19-4
## [15] e1071_1.7-4 spam_2.6-0 highr_0.8 pillar_1.5.1 Rdpack_2.1.1 miscTools_0.6-26 rlang_0.4.10
## [22] spdep_1.1-5 gdata_2.18.0 raster_3.4-5 gmodels_2.18.1 Matrix_1.3-2 splines_4.0.5 stringr_1.4.0
## [29] xfun_0.22 compiler_4.0.5 pkgconfig_2.0.3 maxLik_1.4-6 tidyselect_1.1.0 tibble_3.0.6 expm_0.999-6
## [36] codetools_0.2-18 fansi_0.4.2 crayon_1.4.1 dplyr_1.0.5 sf_0.9-7 MASS_7.3-53.1 rbibutils_2.0
## [43] spatialreg_1.1-5 grid_4.0.5 nlme_3.1-152 spData_0.3.8 lifecycle_1.0.0 DBI_1.1.1 magrittr_2.0.1
## [50] units_0.7-0 ibdreg_0.3.1 KernSmooth_2.23-18 stringi_1.5.3 pryr_0.1.4 LearnBayes_2.15.1 sp_1.4-5
## [57] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.6 boot_1.3-27 sandwich_3.0-0 Formula_1.2-4 tools_4.0.5
## [64] forcats_0.5.1 glue_1.4.2 markdown_1.1 purrr_0.3.4 parallel_4.0.5 classInt_0.4-3 dotCall64_1.0-1
data(Produc)
data(usaww)
head(Produc[c("gsp", "pcap", "pc", "hwy")])
## gsp pcap pc hwy
## 1 28418 15032.67 35793.80 7325.80
## 2 29375 15501.94 37299.91 7525.94
## 3 31303 15972.41 38670.30 7765.42
## 4 33430 16406.26 40084.01 7907.66
## 5 33749 16762.67 42057.31 8025.52
## 6 33604 17316.26 43971.71 8158.23
# Baseline model. No instruments
GM <- spgm(log(gsp) ~ log(pcap) + log(pc), data=Produc,
lag = TRUE,
spatial.error = FALSE,
method = "w2sls",
listw = usaww)
## Warning in if (model == "fixed" & !isTRUE(attr(terms(formula), "intercept"))) formula <- as.formula(paste(attr(terms(formula), : the condition has
## length > 1 and only the first element will be used
summary(GM)
##
## Call:spgm(formula = log(gsp) ~ log(pcap) + log(pc), data = Produc, listw = usaww, lag = TRUE, spatial.error = FALSE, method = "w2sls")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2054157 -0.0290282 0.0017208 0.0260133 0.2340702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## lambda 0.367638 0.043093 8.5313 < 2e-16
## log(pcap) 0.087213 0.034721 2.5118 0.01201
## log(pc) 0.498388 0.037026 13.4603 < 2e-16
##
## Residual variance (sigma squared): 0.0023562, (sigma: 0.048541)
# Model with instruments (log(hwy) is being used to instrument log(pcap))
GM_instru <- spgm(log(gsp) ~ log(pcap) + log(pc), data=Produc,
lag = TRUE,
spatial.error = FALSE,
method = "w2sls",
instruments = ~log(hwy) + log(pc),
listw = usaww)
## Warning in if (model == "fixed" & !isTRUE(attr(terms(formula), "intercept"))) formula <- as.formula(paste(attr(terms(formula), : the condition has
## length > 1 and only the first element will be used
summary(GM_instru)
##
## Call:spgm(formula = log(gsp) ~ log(pcap) + log(pc), data = Produc, listw = usaww, lag = TRUE, spatial.error = FALSE, instruments = ~log(hwy) +
## log(pc), method = "w2sls")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2054157 -0.0290282 0.0017208 0.0260133 0.2340702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## lambda 0.367638 0.043093 8.5313 < 2e-16
## log(pcap) 0.087213 0.034721 2.5118 0.01201
## log(pc) 0.498388 0.037026 13.4603 < 2e-16
##
## Residual variance (sigma squared): 0.0023562, (sigma: 0.048541)
为什么检测不起作用？
因为您没有正确指定模型。 尝试：
GM_instru <- spgm(log(gsp) ~ log(pc), data=Produc,
lag = TRUE,
spatial.error = FALSE, endog = ~log(pcap),
method = "w2sls",
instruments = ~log(hwy),
listw = usaww)
其实内生变量是有自变量的，不应该在公式对象中指定。